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toppersexam · 5 years ago

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UGC NET Commerce Books, Question Paper, Free Study Material, MCQ

UGC NET Commerce Books, Question Paper, Free Study Material, MCQ The National Eligibility Test, also known as UGC NET or NTA-UGC-NET, is the test for determining the eligibility for the post of Assistant Professor and/or Junior Research Fellowship award in Indian universities and colleges. UGC NET is considered as one of the toughest exams in India, with success ratio of merely 6%. UGC NET Commerce Question Paper and MCQs Buy the question bank or online quiz of UGC NET Commerce Exam Going through the UGC NET Commerce Exam Question Bank is a must for aspirants to both understand the exam structure as well as be well prepared to attempt the exam. The first step towards both preparation as well as revision is to practice from UGC NET Commerce Exam with the help of Question Bank or Online quiz. We will provide you the questions with detailed answer. UGC NET Commerce Question Paper and MCQs : Available Now UGC NET Commerce Free Study Material : Click Here UGC NET Commerce Books : Click Here UGC NET Commerce Syllabus Unit 1 – Business Environment and International Business Concepts and elements of business environment: Economic environment- Economic systems, Economic policies(Monetary and fiscal policies); Political environment Role of government in business; Legal environment- Consumer Protection Act, FEMA; Socio-cultural factors and their influence on business; Corporate Social Responsibility (CSR), Scope and importance of international business; Globalization and its drivers; Modes of entry into international business, Theories of international trade; Government intervention in international trade; Tariff and non-tariff barriers; India’s foreign trade policy, Foreign direct investment (FDI) and Foreign portfolio investment (FPI); Types of FDI, Costs and benefits of FDI to home and host countries; Trends in FDI; India’s FDI policy, Balance of payments (BOP): Importance and components of BOP, Regional Economic Integration: Levels of Regional Economic Integration; Trade creation and diversion effects; Regional Trade Agreements: European Union (EU), ASEAN, SAARC, NAFTA International Economic institutions: IMF, World Bank, UNCTAD, World Trade Organisation (WTO): Functions and objectives of WTO; Agriculture Agreement; GATS; TRIPS; TRIMS Unit 2 – Accounting and Auditing Basic accounting principles; concepts and postulates, Partnership Accounts: Admission, Retirement, Death, Dissolution and Insolvency of partnership firms, Corporate Accounting: Issue, forfeiture and reissue of shares; Liquidation of companies; Acquisition, merger, amalgamation and reconstruction of companies, Holding company accounts, Cost and Management Accounting: Marginal costing and Break-even analysis; Standard costing; Budgetary control; Process costing; Activity Based Costing (ABC); Costing for decision-making; Life cycle costing, Target costing, Kaizen costing and JIT, Financial Statements Analysis: Ratio analysis; Funds flow Analysis; Cash flow analysis, Human Resources Accounting; Inflation Accounting; Environmental Accounting, Indian Accounting Standards and IFRS, Auditing: Independent financial audit; Vouching; Verification ad valuation of assets and liabilities; Audit of financial statements and audit report; Cost audit, Recent Trends in Auditing: Management audit; Energy audit; Environment audit; Systems audit; Safety audit Unit 3 – Business Economics Meaning and scope of business economics, Objectives of business firms, Demand analysis: Law of demand; Elasticity of demand and its measurement; Relationship between AR and MR, Consumer behavior: Utility analysis; Indifference curve analysis, Law of Variable Proportions: Law of Returns to Scale, Theory of cost: Short-run and long-run cost curves, Price determination under different market forms: Perfect competition; Monopolistic competition; Oligopoly- Price leadership model; Monopoly; Price discrimination, Pricing strategies: Price skimming; Price penetration; Peak load pricing Unit 4 – Business Finance Scope and sources of finance; Lease financing, Cost of capital and time value of money, Capital structure, Capital budgeting decisions: Conventional and scientific techniques of capital budgeting analysis, Working capital management; Dividend decision: Theories and policies, Risk and return analysis; Asset securitization, International monetary system, Foreign exchange market; Exchange rate risk and hedging techniques, International financial markets and instruments: Euro currency; GDRs; ADRs, International arbitrage; Multinational capital budgeting Unit 5 – Business Statistics and Research Methods Measures of central tendency, Measures of dispersion, Measures of skewness, Correlation and regression of two variables, Probability: Approaches to probability; Bayes’ theorem, Probability distributions: Binomial, poisson and normal distributions, Research: Concept and types; Research designs, Data: Collection and classification of data, Sampling and estimation: Concepts; Methods of sampling – probability and nonprobability methods; Sampling distribution; Central limit theorem; Standard error; Statistical estimation, Hypothesis testing: z-test; t-test; ANOVA; Chi–square test; Mann-Whitney test (Utest); Kruskal Wallis test (H-test); Rank correlation test, Report writing Unit 6 – Business Management and Human Resource Management Principles and functions of management, Organization structure: Formal and informal organizations; Span of control, Responsibility and authority: Delegation of authority and decentralization Motivation and leadership: Concept and theories, Corporate governance and business ethics, Human resource management: Concept, role and functions of HRM; Human resource planning; Recruitment and selection; Training and development; Succession planning, Compensation management: Job evaluation; Incentives and fringe benefits, Performance appraisal including 360 degree performance appraisal, Collective bargaining and workers’ participation in management, Personality: Perception; Attitudes; Emotions; Group dynamics; Power and politics; Conflict and negotiation; Stress management, Organizational Culture: Organizational development and organizational change Unit 7 – Banking and Financial Institutions Overview of Indian financial system, Types of banks: Commercial banks; Regional Rural Banks (RRBs); Foreign banks; Cooperative banks, Reserve Bank of India: Functions; Role and monetary policy management, Banking sector reforms in India: Basel norms; Risk management; NPA management, Financial markets: Money market; Capital market; Government securities market, Financial Institutions: Development Finance Institutions (DFIs); Non-Banking Financial Companies (NBFCs); Mutual Funds; Pension Funds, Financial Regulators in India, Financial sector reforms including financial inclusion, Digitisation of banking and other financial services: Internet banking; mobile banking; Digital payments systems, Insurance: Types of insurance- Life and Non-life insurance; Risk classification and management; Factors limiting the insurability of risk; Re-insurance; Regulatory framework of insurance- IRDA and its role. Unit 8 – Marketing Management Marketing: Concept and approaches; Marketing channels; Marketing mix; Strategic marketing planning; Market segmentation, targeting and positioning, Product decisions: Concept; Product line; Product mix decisions; Product life cycle; New product development, Pricing decisions: Factors affecting price determination; Pricing policies and strategies, Promotion decisions: Role of promotion in marketing; Promotion methods – Advertising; Personal selling; Publicity; Sales promotion tools and techniques; Promotion mix, Distribution decisions: Channels of distribution; Channel management, Consumer Behaviour; Consumer buying process; factors influencing consumer buying decisions, Service marketing, Trends in marketing: Social marketing; Online marketing; Green marketing; Direct marketing; Rural marketing; CRM, Logistics management. Unit 9: Legal Aspects of Business Indian Contract Act, 1872: Elements of a valid contract; Capacity of parties; Free consent; Discharge of a contract; Breach of contract and remedies against breach; Quasi contracts, Special contracts: Contracts of indemnity and guarantee; contracts of bailment and pledge; Contracts of agency, Sale of Goods Act, 1930: Sale and agreement to sell; Doctrine of Caveat Emptor; Rights of unpaid seller and rights of buyer, Negotiable Instruments Act, 1881: Types of negotiable instruments; Negotiation and assignment; Dishonour and discharge of negotiable instruments, The Companies Act, 2013: Nature and kinds of companies; Company formation; Management, meetings and winding up of a joint stock company, Limited Liability Partnership: Structure and procedure of formation of LLP in India, The Competition Act, 2002: Objectives and main provisions, The Information Technology Act, 2000: Objectives and main provisions; Cyber crimes and penalties, The RTI Act, 2005: Objectives and main provisions, Intellectual Property Rights (IPRs) : Patents, trademarks and copyrights; Emerging issues in intellectual property, Goods and Services Tax (GST): Objectives and main provisions; Benefits of GST; Implementation mechanism; Working of dual GST. Unit 10: Income-tax and Corporate Tax Planning Income-tax: Basic concepts; Residential status and tax incidence; Exempted incomes; Agricultural income; Computation of taxable income under various heads; Deductions from Gross total income; Assessment of Individuals; Clubbing of incomes, International Taxation: Double taxation and its avoidance mechanism; Transfer pricing, Corporate Tax Planning: Concepts and significance of corporate tax planning; Tax avoidance versus tax evasion; Techniques of corporate tax planning; Tax considerations in specific business situations: Make or buy decisions; Own or lease an asset; Retain; Renewal or replacement of asset; Shut down or continue operations, Deduction and collection of tax at source; Advance payment of tax; E-filing of income-tax returns. NTA UGC NET Commerce Exam Pattern 2020 1. Paper I : It consists of 50 questions from UGC NET teaching & research aptitude exam (general paper), which you have to attempt in 1 hour. 2. Paper II : The UGC Commerce exam (paper 2) will have 100 questions and the total duration will be two hours. Each question carries 2 marks, so the exam will be worth 200 marks. Read below to know the pattern of NET Commerce examination (part II). Exam HighlightsDetails Test Duration120 minutes Total Questions100 Marks per question2 Total Marks200 Negative MarkingN/A Free Mock Test UGC NET Commerce : Click Here Online Test Series UGC NET Commerce : Click Here #UGCNETCommerce #UGCNETCommerce2020 #UGCNETCommerceExam #FreeTestSeries #QuestionsBank #UGCNETCommerceSyllabus #OnlineTestSeries #OnlineMockTest #ImportantQuestionPaper #ImportantQuestion

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juniperpublishers-robotics-blog · 5 years ago

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Control Parameters in Differential Evolution (DE): A Short Review- Juniper Publishers

Abstract

Differential Evolution (DE) is a population based stochastic search algorithm for optimization. DE has three main control parameters, Crossover (cr), Mutation factor (F) and Population size (NP). These control parameters play a vital and crucial rule in improving the performance of search process in DE. This paper introduces a brief review for control parameters in Differential evolution (DE).

Keywords: Differential evolution; Population size; Global optimization; Control parameters

Abbrevations: DE: Differential Evolution; Cr: Crossover; NP: Population Size; F: Mutation Factor

Introduction

Differential Evolution (DE) is a population-based heuristic algorithm proposed by Storn & Price [1] to solve global optimization problems with different characteristics over continuous space. Despite its simplicity, it proved a great performance in solving non-differentiable, non-continuous and multi-modal optimization problems [2]. DE has three main control parameters which are the crossover (CR), mutation factor (F) and population size (NP). The values of the control parameters affect significantly on the performance of DE. Therefore, the tuning of those control parameters is considered a challenging task. DE has a great performance in exploring the solution space and this is considered as the main advantage, on the other side, an obvious weak point is its poor performance in exploitation phase which may cause a stagnation and/or premature convergence.

The next section introduces differential evolution. Section 3 introduces a short review for control parameters in DE. And finally, the paper is concluded in section 4.

Differential Evolution

In simple DE, DE/rand/1/bin [1,2], an initial population of NP individuals jX, j=1,2,..,NP, is generated at random according to a uniform distribution within lower and upper boundaries (,)LUjjxx. Individuals are evolved by the means of crossover and mutation to generate a trial vector. The trial vector competes with his parent in order to select the fittest to the next generation. The steps of DE are:

Initialization of a population

Initial population in DE, as the starting point for the process of optimization, is created by assigning a random chosen value for each decision variable in every vector, as indicated in equation (1).

Where Lj,Uj: the lower and upper boundaries for xj, rj and: a random number uniform [0, 1].

Mutation

A mutant vector viG+1 is generated for each target vector xiG at generation G according to equation (2)

Where r1,r2, r3 are randomly chosen from the population. The mutation factor F∈[0,2]. A new value for the component of mutant vector is generated using (1) if it violates the boundary constraints.

Recombination (crossover)

Crossover is the process of swapping information between the target and the mutated individuals using (3), to yield the trial vector uiG+1 Two types of crossover can be used, binomial crossover or exponential crossover.

where j =1, 2,.., D , rand( j)∈[0,1] is the jth evaluation of a uniform random number. Crossover rate (CR) is between 0 and 1, r and (i) is a random index between 1 and D to ensures that uiG+1; gets at least one element from viG+1; otherwise, the population remains without change.

In the exponential crossover, a starting index l and a number of components w are chosen randomly

from the ranges {l,D}and {l,D −1}respectively. The values of variables in locations l to l + w from viG+1 and the remaining locations from the xiG are used to produce the trial vector uiG+1

Selection

Greedy scheme for fast convergence of DE. The child uiG+1 is compared with its parent xiG to select the better for the next generation according to the selection scheme in equation (4).

A detailed description of standard DE algorithm is given in Figure 1.

Short Review

During the last two decades, the problem of finding the balance between the exploration and exploitation has attracted many researchers in order to improve the performance of DE by developing new mutation strategies or hybridizing promising mutation strategies.

Das et al. [3] proposed an improved variant of DE/targetto- best/1/bin based on the concept of population members’ neighborhood. Zhang & Sanderson [4] proposed a new mutation strategy ��DE/current-to-pbest” with an optional external archive that utilizes the historical data in order to progress towards the promising direction and called it JADE. Qin, Huang & Suganthan [5] proposed SaDE, in which a self-adaptive mechanism for trial vector generation is presented, that is based on the idea of learning from the past experience in generating promising solutions. Mohamed et al. [6-8] proposed a novel mutation strategy which is based on the weighted difference between the best and the worst individual during a specific generation, the new mutation strategy is combined with the basic mutation DE/rand/1/bin with equal probability for selecting each of them. Li & Yin [9] used two mutation strategies based on the best and random vectors. Mohamed [10] proposed IDE, in which new triangular mutation rule that selects three random vectors and adding the difference vector between the best and worst to the better vector. The new mutation rule is combined with the basic mutation rule through a non-linear decreasing probability rule. And a restart mechanism to avoid the premature convergence is presented. Recently, triangular mutation has been also used to solve IEEE CEC 2013 unconstrained problems [11], constrained non-linear integer and mixed-integer global optimization problems [12], IEEE CEC2006 constrained optimization problems [13], CEC 2010 large-scale optimization problems [14], and stochastic programming problems [15].

Extensive research was presented for controlling the parameters, as control parameters play a vital role in the evolution process. Brest et al. [16] presented a new self-adaptive technique for controlling the parameters. Noman & Iba [17] proposed an adaptive crossover based on local search and the length of the search was adjusted using hill-climbing. Peng et al. [18] proposed rJADE, in which a weighting strategy is added to JADE, with a “restart with knowledge transfer” method in order to benefit from the knowledge obtained from the previous failure. Montgomery & Chen [19] presented a complete analysis of how much the evolution process affected by the value of CR. Mallipeddi et al. [20] proposed a pool of values for each control parameter to select the appropriate value during the evolution process. Wang, Cai & Zhang [21] proposed a new method that randomly chooses from a pool that contains three strategies in order to generate the trial vector and three control parameter settings, they called it CoDE. Yong et al. [22] presented CoBiDE, in which a covariance matrix learning for the crossover operator and a bimodal distribution parameter to control the parameters are introduced. Draa, Bouzoubia & Boukhalfa [23] introduced a new sinusoidal formula in order to adjust the values of crossover and the scaling factor, they called it SinDE. A complete review could be found in [24,25].

DE mechanism depends on selecting three random individuals from the population to perform the mutation process. Therefore, the population size must be greater than the selected vectors. Large population size increases the diversity but consumes more resources (function calls), while small population size may cause stagnation or tripping in local optima. Thus, the choosing of the population size is considered a very critical aspect. From the literature, it has been found that researchers choose the population size in four different ways.

i. Choosing the population size for each problem separately based on the experience or previous knowledge and keep it constant during all runs [26,27].

ii. Relate the population size to the problem dimensionality [6,28,29].

iii. Setting the population size fixed during all runs and independent of the dimension of the problems [22,30].

iv. Allowing the population size to vary during the runs using adaptation rule [31-33]. A complete review of population size could be found in [34,35].

Conclusion

Control parameters plays a vital rule in the evolution process of the DE. Over the last decades, many EAs have been proposed to solve optimization problems. However, all these algorithms including DE have the same shortcomings in solving optimization problems. One of them is the choice of the control parameters which are difficult to adjust for different problems with different characteristics. This paper introduced a brief review for a considerable number of research studies that have been proposed to enhance the performance of DE.

For More Open Access Journals Please Click on: Juniper Publishers

Fore More Articles Please Visit: Robotics & Automation Engineering Journal

#Juniper Publishers #Open Access Journals #Robotics #Self-ruling robots #Neural Networks

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sciforce · 6 years ago

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Top AI algorithms for Healthcare

The benefits of AI for healthcare have been extensively discussed in the recent years up to the point of the possibility to replace human physicians with AI in the future.

Both such discussions and the current AI-driven projects reveal that Artificial Intelligence can be used in healthcare in several ways:

AI can learn features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice in treatment design or risk assessment;

AI system can extract useful information from a large patient population to assist making real-time inferences for health risk alert and health outcome prediction;

AI can do repetitive jobs, such as analyzing tests, X-Rays, CT scans or data entry;

AI systems can help to reduce diagnostic and therapeutic errors that are inevitable in the human clinical practice;

AI can assist physicians by providing up-to-date medical information from journals, textbooks and clinical practices to inform proper patient care;

AI can manage medical records and analyze both performance of an individual institution and the whole healthcare system;

AI can help develop precision medicine and new drugs based on the faster processing of mutations and links to disease;

AI can provide digital consultations and health monitoring services — to the extent of being “digital nurses” or “health bots”.

Despite the variety of applications of AI in the clinical studies and healthcare services, they fall into two major categories: analysis of structured data, including images, genes and biomarkers, and analysis of unstructured data, such as notes, medical journals or patients’ surveys to complement the structured data. The former approach is fueled by Machine Learning and Deep Learning Algorithms, while the latter rest on the specialized Natural Language Processing practices.

Figure 1. Machine Learning and Natural Language Processing in healthcare.

Machine Learning Algorithms

ML algorithms chiefly extract features from data, such as patients’ “traits” and medical outcomes of interest.

Figure 2. The most popular Machine Learning algorithms used in the medical literature. The data are generated through searching the Machine Learning algorithms within healthcare on PubMed

For a long time, AI in healthcare was dominated by the logistic regression, the most simple and common algorithm when it is necessary to classify things. It was easy to use, quick to finish and easy to interpret. However, in the past years the situation has changed and SVM and neural networks have taken the lead.

Support Vector Machine

Support Vector Machines (SVM) can be employed for classification and regression, but this algorithm is chiefly used in classification problems that require division of a dataset into two classes by a hyperplane. The goal is to choose a hyperplane with the greatest possible margin , or distance between the hyperplane and any point within the training set, so that new data can be classified correctly. Support vectors are data points that are closest to the hyperplane and that, if removed, would alter its position. In SVM, the determination of the model parameters is a convex optimization problem so the solution is always global optimum.

Figure 3. Support Vector Machine

SVMs are used extensively in clinical research, for example, to identify imaging biomarkers, to diagnose cancer or neurological diseases and in general for classification of data from imbalanced datasets or datasets with missing values.

Neural networks

In neural networks, the associations between the outcome and the input variables are depicted through hidden layer combinations of prespecified functionals. The goal is to estimate the weights through input and outcome data in such a way that the average error between the outcome and their predictions is minimized.

Figure 4. Neural Network

Neural networks are successfully applied to various areas of medicine, such as diagnostic systems, biochemical analysis, image analysis, and drug development, with the textbook example of breast cancer prediction from mammographic images.

Logistic Regression

Logistic Regression is one of the basic and still popular multivariable algorithms for modeling dichotomous outcomes. Logistic regression is used to obtain odds ratio when more than one explanatory variable is present. The procedure is similar to multiple linear regression, with the exception that the response variable is binomial. It shows the impact of each variable on the odds ratio of the observed event of interest. In contrast to linear regression, it avoids confounding effects by analyzing the association of all variables together.

Figure 5. Logistic Regression

In healthcare, logistic regression is widely used to solve classification problems and to predict the probability of a certain event, which makes it a valuable tool for a disease risk assessment and improving medical decisions.

Natural Language Processing

In healthcare, a large proportion of clinical information is in the form of narrative text, such as physical examination, clinical laboratory reports, operative notes and discharge summaries, which are unstructured and incomprehensible for the computer program without special methods of text processing. Natural Language Processing addresses these issues as it identifies a series of disease-relevant keywords in the clinical notes based on the historical databases that after validation enter and enrich the structured data to support clinical decision making.

TF-IDF

Basic algorithm for extracting keywords, TF-IDF stands for term frequency-inverse document frequency. The TF-IDF weight is a statistical measure of a word importance to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus.

Figure 6. TF-IDF

In healthcare, TF-IDF is used in finding patients’ similarity in observational studies, as well as in discovering disease correlations from medical reports and finding sequential patterns in databases.

Naïve Bayes

Naïve Bayes classifier is a baseline method for text categorization, the problem of judging documents as belonging to one category or the other. Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Even if these features are interdependent, all of these properties independently contribute to the probability of belonging to a certain category.

Figure 7. Naïve Bayes classifier

It remains one of the most effective and efficient classification algorithms and has been successfully applied to many medical problems, such as classification of medical reports and journal articles.

Word Vectors

Considered to be a breakthrough in NLP, word vectors, or word2vec, is a group of related models that are used to produce word embeddings. In their essence, word2vec models are shallow, two-layer neural networks that reconstruct linguistic contexts of words. Word2vec produces a multidimensional vector space out of a text, with each unique word having a corresponding vector. Word vectors are positioned in the vector space in a way that words that share contexts are located in close proximity to one another.

Figure 8. Word vectors

Word vectors are used for biomedical language processing, including similarity finding, medical terms standardization and discovering new aspects of diseases.

Deep Learning

Deep Learning is an extension of the classical neural network technique, being, to put it simply, as a neural network with many layers. Having more capacities compared to classical ML algorithms, Deep Learning can explore more complex non-linear patterns in the data. Being a pipeline of modules each of them are trainable, Deep Learning represents a scalable approach that, among others, can perform automatic feature extraction from raw data.

In the medical applications, Deep Learning algorithms successfully address both Machine Learning and Natural Language Processing tasks. The commonly used Deep Learning algorithms include convolution neural network (CNN), recurrent neural network, deep belief network and multilayer perception, with CNNs leading the race from 2016 on.

Convolutional Neural Network

The CNN was developed to handle high-dimensional data, or data with a large number of traits, such as images. Initially, as proposed by LeCun, the inputs for CNN were normalized pixel values on the images. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex, with individual cortical neurons responding to stimuli only in a restricted region of the receptive field. However, the receptive fields of different neurons partially overlap such that they cover the entire visual field. The CNN then transfers the pixel values in the image by weighting in the convolution layers and sampling in the subsampling layers alternatively. The final output is a recursive function of the weighted input values.

Figure 9. A convolutional neural network

Recently, the CNN has been successfully implemented in the medical area to assist disease diagnosis, such as skin cancer or cataracts.

Recurrent Neural Network

The second in popularity in healthcare, RNNs represent neural networks that make use of sequential information. RNNs are called recurrent because they perform the same task for every element of a sequence, and the output depends on the previous computations. RNNs have a “memory” which captures information about what has been calculated several steps back (more on this later).

Figure 10. A recurrent neural network

Extremely popular in NLP, RNNs are also a powerful method of predicting clinical events.

Until recently, the AI applications in healthcare chiefly addressed a few disease types: cancer, nervous system disease and cardiovascular disease being the biggest ones. At present, advances in AI and NLP, and especially the development of Deep Learning algorithms have turned the healthcare industry to using AI methods in multiple spheres, from dataflow management to drug discovery.

#machine learning #artificial intelligence #healthcare #nlp #technology

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evoldir · 8 years ago

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Course: Lausanne.Statistical_Genetics.Sep4-15

--_000_150084433338370825unilch_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable SWISS INSTITUTE IN STATISTICAL GENETICS MODULE 1 - BAYESIAN STATISTICS FOR GENETICS WHERE? University of Lausanne WHEN? 4-6 September 2017 ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US) INSTRUCTORS: Prof. Jonathan Wakefield (University of Washington - US) & Prof. Ken Rice (University of Washington - US) INFO & REGISTRATION: http://bit.ly/2vQyRDW DESCRIPTION: The use of Bayesian methods in genetics has a long history. In this introductory module we will begin by discussing introductory probability. We will then describe Bayesian approaches to binomial proportions, multinomial proportions, two-sample comparisons (binomial, Poisson, normal), the linear model, and Monte Carlo methods of summarization. Advanced topics will be touched on, including hierarchical models, generalized linear models, and missing data. Illustrative applications will include: Hardy-Weinberg testing and estimation, detection of allele-specific expression, QTL mapping, testing in genome-wide association studies, mixture models, multiple testing in high throughput genomics. MODULE 2 - POPULATION GENETIC DATA ANALYSIS WHERE? University of Lausanne WHEN? 6-8 September 2017 ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US) INSTRUCTORS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US) INFO & REGISTRATION: http://bit.ly/2uYfa0I DESCRIPTION: A unified treatment for the analysis of discrete genetic data, starting with estimates and sample variances of allele frequencies to illustrate genetic vs statistical sampling and Bayesian approaches. A detailed look at Hardy-Weinberg and linkage disequilibrium, including the use of exact tests with mid-p-values and a new look at X-chromosome Hardy- Weinberg testing. A new characterization of population structure with F-statistics, based on allelic matching within and between populations with individual relationship estimation as a special case. Analyses illustrated with applications to forensic science and association mapping, with particular reference to rare variants. MODULE 3 - QUANTITATIVE GENETICS WHERE? University of Lausanne WHEN? 11-13 September 2017 ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US) INSTRUCTORS: Prof. Bruce Walsh (University of Arizona - US) & Prof. Guilherme J. M. Rosa (University of Wisconsin - US) INFO & REGISTRATION: http://bit.ly/2vQCDgO DESCRIPTION: Quantitative Genetics is the analysis of complex characters where both genetic and environment factors contribute to trait variation. Since this includes most traits of interest, such as disease susceptibility, crop yield, growth and reproduction in animals, human and animal behavior, and all gene expression data (transcriptome and proteome), a working knowledge of quantitative genetics is critical in diverse fields from plant and animal breeding, human genetics, genomics, behavior, to ecology and evolutionary biology. The course will cover the basics of quantitative genetics including: genetic basis for complex traits, population genetic assumptions including detection of admixture, Fisher's variance decomposition, covariance between relatives, calculation of the numerator relationship matrix based on IBD alleles and an arbitrary pedigree, the genomic relationship matrix based on AIS alleles, heritability in the broad and narrow sense, inbreeding and crossbreedi ng, and response to selection. Also an introduction to advanced topics such as: Mixed Models, Best Linear Unbiased Prediction (BLUP), Genomic selection (GBLUP), Genome Wide Association Analysis (GWAS), QTL mapping, detection of selection from genomic data, correlated characters; and the multivariate response to selection. MODULE 4 - MIXED MODELS IN QUANTITATIVE GENETICS WHERE? University of Lausanne WHEN? 13-15 September 2017 ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US) INSTRUCTORS: Prof. Bruce Walsh (University of Arizona - US) & Prof. Guilherme J. M. Rosa (University of Wisconsin - US) INFO & REGISTRATION: http://bit.ly/2uY59Rk DESCRIPTION: "Mixed models" refers to the analysis of linear models with arbitrary (co)variance structures among and within random effects and may be due to such factors as relationships or shared environments, cytoplasm, maternal effects and history. Mixed models are utilized in complex data analysis where the usual assumption(s) of independence and/or homogenous variances fail. Mixed models allow effects of nature to be separated from those of nurture and are emerging as the default method of analysis for human data. These issues are pervasive in human studies due to the lack of ability to randomize subjects to households, choice, and prior history. In plant breeding, growth and yield data are correlated due to shared locations, but diminish by distance resulting in spatial correlations. In animal breeding, performance data is correlated because individuals maybe related and may share common material environment as well as common pens or cages. Further, when individuals sha re a common space, they may experience indirect genetics effects (IGEs), which is an inherited effect in one individual experienced as an environmental effect in an associated individual. The evolution of cooperation and competition is based on IGEs, the estimation of which require mixed model analysis. Detection of cytoplasmic and epigenetic effects rely heavily on mixed model methods because of shared material or parental histories. Topics to be discussed include a basic matrix algebra review, the general linear model, derivation of the mixed model, BLUP and REML estimation, estimation and design issues, Bayesian formulations. Applications to be discussed include estimation of breeding values and genetic variances in general pedigrees, association mapping, genomic selection, spatial correlations and corrections, maternal genetic effects, detecting selection from genomic data, admixture detection and correction, direct and indirect genetic effects, models of general group a nd kin selection, genotype by environment interaction models QUERIES: [email protected] --_000_150084433338370825unilch_ Content-Type: text/html; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable

SWISS INSTITUTE IN STATISTICAL GENETICS

MODULE 1 - BAYESIAN STATISTICS FOR GENETICS

WHERE? University of Lausanne

WHEN? 4-6 September 2017

ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US)

INSTRUCTORS: Prof. Jonathan Wakefield (University of Washington - US) & Prof. Ken Rice (University of Washington - US)

INFO & REGISTRATION: http://bit.ly/2vQyRDW

DESCRIPTION: The use of Bayesian methods in genetics has a long history. In this introductory module we will begin by discussing introductory probability. We will then describe Bayesian approaches to binomial proportions, multinomial proportions, two-sample comparisons (binomial, Poisson, normal), the linear model, and Monte Carlo methods of summarization. Advanced topics will be touched on, including hierarchical models, generalized linear models, and missing data. Illustrative applications will include: Hardy-Weinberg testing and estimation, detection of allele-specific expression, QTL mapping, testing in genome-wide association studies, mixture models, multiple testing in high throughput genomics.

MODULE 2 - POPULATION GENETIC DATA ANALYSIS

WHERE? University of Lausanne

WHEN? 6-8 September 2017

ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US)

INSTRUCTORS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US)

INFO & REGISTRATION: http://bit.ly/2uYfa0I

DESCRIPTION: A unified treatment for the analysis of discrete genetic data, starting with estimates and sample variances of allele frequencies to illustrate genetic vs statistical sampling and Bayesian approaches. A detailed look at Hardy-Weinberg and linkage disequilibrium, including the use of exact tests with mid-p-values and a new look at X-chromosome Hardy- Weinberg testing. A new characterization of population structure with F-statistics, based on allelic matching within and between populations with individual relationship estimation as a special case. Analyses illustrated with applications to forensic science and association mapping, with particular reference to rare variants.

MODULE 3 - QUANTITATIVE GENETICS

WHERE? University of Lausanne

WHEN? 11-13 September 2017

ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US)

INSTRUCTORS: Prof. Bruce Walsh (University of Arizona - US) & Prof. Guilherme J. M. Rosa (University of Wisconsin - US)

INFO & REGISTRATION: http://bit.ly/2vQCDgO

DESCRIPTION: Quantitative Genetics is the analysis of complex characters where both genetic and environment factors contribute to trait variation. Since this includes most traits of interest, such as disease susceptibility, crop yield, growth and reproduction in animals, human and animal behavior, and all gene expression data (transcriptome and proteome), a working knowledge of quantitative genetics is critical in diverse fields from plant and animal breeding, human genetics, genomics, behavior, to ecology and evolutionary biology. The course will cover the basics of quantitative genetics including: genetic basis for complex traits, population genetic assumptions including detection of admixture, Fisher's variance decomposition, covariance between relatives, calculation of the numerator relationship matrix based on IBD alleles and an arbitrary pedigree, the genomic relationship matrix based on AIS alleles, heritability in the broad and narrow sense, inbreeding and crossbreeding, and response to selection. Also an introduction to advanced topics such as: Mixed Models, Best Linear Unbiased Prediction (BLUP), Genomic selection (GBLUP), Genome Wide Association Analysis (GWAS), QTL mapping, detection of selection from genomic data, correlated characters; and the multivariate response to selection.

MODULE 4 - MIXED MODELS IN QUANTITATIVE GENETICS

WHERE? University of Lausanne

WHEN? 13-15 September 2017

ORGANIZERS: Prof. Jrme Goudet (University of Lausanne - CH) & Prof. Bruce Weir (University of Washington - US)

INSTRUCTORS: Prof. Bruce Walsh (University of Arizona - US) & Prof. Guilherme J. M. Rosa (University of Wisconsin - US)

INFO & REGISTRATION: http://bit.ly/2uY59Rk

DESCRIPTION: "Mixed models" refers to the analysis of linear models with arbitrary (co)variance structures among and within random effects and may be due to such factors as relationships or shared environments, cytoplasm, maternal effects and history. Mixed models are utilized in complex data analysis where the usual assumption(s) of independence and/or homogenous variances fail. Mixed models allow effects of nature to be separated from those of nurture and are emerging as the default method of analysis for human data. These issues are pervasive in human studies due to the lack of ability to randomize subjects to households, choice, and prior history. In plant breeding, growth and yield data are correlated due to shared locations, but diminish by distance resulting in spatial correlations. In animal breeding, performance data is correlated because individuals maybe related and may share common material environment as well as common pens or cages. Further, when individuals share a common space, they may experience indirect genetics effects (IGEs), which is an inherited effect in one individual experienced as an environmental effect in an associated individual. The evolution of cooperation and competition is based on IGEs, the estimation of which require mixed model analysis. Detection of cytoplasmic and epigenetic effects rely heavily on mixed model methods because of shared material or parental histories. Topics to be discussed include a basic matrix algebra review, the general linear model, derivation of the mixed model, BLUP and REML estimation, estimation and design issues, Bayesian formulations. Applications to be discussed include estimation of breeding values and genetic variances in general pedigrees, association mapping, genomic selection, spatial correlations and corrections, maternal genetic effects, detecting selection from genomic data, admixture detection and correction, direct and indirect genetic effects, models of general group and kin selection, genotype by environment interaction models

QUERIES: [email protected]

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honeycvntcake · 4 years ago

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awesomecvworldblog · 4 years ago