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ms – a program for generating samples under neutral models Richard R. Hudson October 16, 2017 This document describes how to use ms, a program to generate samples under a variety of neutral models. The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or […]
 Bayesian methods in ecology and evolution¶ https://bitbucket.org/mfumagal/statistical_inference Intended Learning Outcomes¶ At the end of this module you will be able to: • critically discuss advantages (and disadvantages) of Bayesian data analysis, • illustrate Bayes’ Theorem and concepts of prior and posterior distributions, • implement simple Bayesian methods, including sampling and approximated techniques and Bayes
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Deep learning¶ Our toy example will be to classify species as endangered or not based genomic data. The rationale is that species with a small (effective population size) will have higher chances to be threatened. The amount of genomic variability (e.g. polymorphic sites and haplotype diversity) is taken as a proxy for the (effective) population
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 Bayesian methods in ecology and evolution¶ https://bitbucket.org/mfumagal/statistical_inference day 4a: approximate Bayesian computation¶ Intended Learning Outcomes¶ At the end of this part you will be able to: • appreciate the applicability of ABC, • describe the rejection algorithm, • critically discuss the choice of summary statistics, • implement ABC methods. The posterior probability \begin{equation} P(\theta|x)
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Supervised machine learning¶ In this practical we will introduce the use of supervised machine learning on biological data. We will discuss nearest neighour classifier, support vector machine, neural networks and deep learning (specifically convolutional neural networks). Our toy example will be to classify species as endangered or not based genomic data. The rationale is that
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 Bayesian methods in ecology and evolution¶ https://bitbucket.org/mfumagal/statistical_inference part 2b: Bayesian inference¶ Once we have specified the prior, we can use Bayes’ theorem to obtain the posterior distribution of model parameters. However, the density (or cumulative) function can be difficult to interpret. Therefore we want to summarise the information enclosed in these distributions. We can
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 Bayesian methods in ecology and evolution¶ https://bitbucket.org/mfumagal/statistical_inference day 1c: Bayesian applications in genomics¶ Reconstructing genomes from sequencing data¶ You are going to develop and implement a Bayesian approach to reconstruct genomes from data produced from high-throughput sequencing machines. Specifically, you will be doing genotype calling from short-read NGS data.  Load the R functions
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Inference¶ Estimators¶ In [ ]: # assume population is Normal(180,5) # E[X]=180 ran1
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