Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics PDF Download
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Author: Daniel Sorensen
Publisher: Springer Science & Business Media
ISBN: 0387954406
Category : Science
Languages : en
Pages : 745
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Book Description
This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Author: Daniel Sorensen
Publisher: Springer Science & Business Media
ISBN: 0387954406
Category : Science
Languages : en
Pages : 745
Get Book Here
Book Description
This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Author: Michael Brimacombe
Publisher: CRC Press
ISBN: 1584887893
Category : Mathematics
Languages : en
Pages : 212
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Book Description
This book emphasizes the importance of the likelihood function in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood function and provide differing but related insights. This is examined here both through review of basic methodology and also the integr
Author: Reihaneh Entezari
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Markov Chain Monte Carlo (MCMC) methods are fundamental tools for sampling highly complex distributions. They are crucial to Bayesian inference as posterior distributions are generally analytically intractable. In this thesis, we tackle two Bayesian inference problems via MCMC methods, that will lie on both methodology and application aspects. The first part of this thesis tackles the computational challenges of Bayesian inference from big data. We develop a new communication-free parallel method, the "Likelihood Inflating Sampling Algorithm (LISA)", that significantly reduces computational costs by randomly splitting the dataset into smaller subsets and running MCMC methods independently in parallel on each subset using different processors. Each processor will be used to run an MCMC chain that samples sub-posterior distributions which are defined using an "inflated" likelihood function. We then discuss on the approaches to combine all sub-samples from all processors to build a highly accurate posterior distribution that is consistent with the full posterior distribution. More importantly, we learn a strategy in combining LISA's draws to study the full posterior of the more complex Bayesian Additive Regression Trees (BART) model, which is highly important in non-parametric regression. We also successfully examine the consistency in performance of LISA on BART with new efficient Metropolis-Hastings proposals introduced by Pratola (2016). The second part of this thesis is focused on the applied aspect of performing Bayesian inference with MCMC methods. We study a Bayesian Geostatistical model to analyze spatial data from the Timiskaming Abitibi River forests in Ontario Canada, provided by the First Resource Management Group Inc.. We implement an MCMC algorithm to perform Bayesian inference on predicting the proportion of hardwood trees from elevation and vegetation index. Spatial predictions are made for new sites in the forests and results are compared with a Logistic Regression model without a spatial effect. We study the trend of accuracy in predictions when fitting fewer data to the model, and present useful insights on the trade-off between performance and the costly need for collecting ground truth data. We further discuss a stratified sampling approach in choosing the subsets of data that allows for potential better predictions.
Author: Sun Luo
Publisher:
ISBN:
Category : Expectation-maximization algorithms
Languages : en
Pages : 194
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Book Description
Author: Kenneth Lange
Publisher: Springer Science & Business Media
ISBN: 9780387953892
Category : Medical
Languages : en
Pages : 404
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Book Description
Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.
Author: Zehua Chen
Publisher: CRC Press
ISBN: 143986831X
Category : Mathematics
Languages : en
Pages : 308
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Book Description
While numerous advanced statistical approaches have recently been developed for quantitative trait loci (QTL) mapping, the methods are scattered throughout the literature. Statistical Methods for QTL Mapping brings together many recent statistical techniques that address the data complexity of QTL mapping. After introducing basic genetics topics an
Author: William Russell Baird
Publisher:
ISBN:
Category : Gene mapping
Languages : en
Pages : 272
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Book Description
Author: Rongling Wu
Publisher: Springer Science & Business Media
ISBN: 038768154X
Category : Science
Languages : en
Pages : 371
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Book Description
This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance vis--vis the more common maximum likelihood-based model selection on both simulated and real market data. All five MCMC methods proved feasible in both cases, although differing in their computational demands. Results on simulated data show that for large degrees of freedom (where the t-distribution becomes more similar to a normal one), Bayesian model selection results in better decisions in favour of the true model than maximum likelihood. Results on market data show the feasibility of all model selection methods, mainly because the distributions appear to be decisively non-Gaussian. (author's abstract).
Author: Nicola J. Camp
Publisher: Springer Science & Business Media
ISBN: 1592591760
Category : Medical
Languages : en
Pages : 362
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Book Description
In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.
