Bayesian approach to inference of population structure

Structurama: Bayesian Inference of Population Structure

Structurama is a program for inferring population structure. Specifically, the program calculates the posterior probability of assigning individuals to different populations. The program takes as input a file containing the allelic information at some number of loci sampled from a collection of individuals. After reading a data file into computer memory, Structurama uses a Gibbs algorithm to sa...

Spectrum: joint bayesian inference of population structure and recombination events

MOTIVATION While genetic properties such as linkage disequilibrium (LD) and population structure are closely related under a common inheritance process, the statistical methodologies developed so far mostly deal with LD analysis and structural inference separately, using specialized models that do not capture their statistical and genetic relationships. Also, most of these approaches ignore the...

Functional Brain Response to Emotional Muical Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference

Introduction: One of the vital skills which has an impact on emotional health and well-being is the regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tools for eliciting and regulating emotion is music. The Anterior Cingulate Cortex (ACC) is part of the emotional neural circuitry involved in Major Depressive Disorder (MDD)....

A Bayesian approach to inferring population structure from dominant markers.

Molecular markers derived from polymerase chain reaction (PCR) amplification of genomic DNA are an important part of the toolkit of evolutionary geneticists. Random amplified polymorphic DNA markers (RAPDs), amplified fragment length polymorphisms (AFLPs) and intersimple sequence repeat (ISSR) polymorphisms allow analysis of species for which previous DNA sequence information is lacking, but do...

A PAC-Bayesian approach to adaptive inference

A Computational Approach to Bayesian Inference

xxx Although the Bayesian approach provides a complete solution to model-based analysis, it is often difficult to obtain closed-form solutions for complex models. However, numerical solutions to Bayesian modeling problems are now becoming attractive because of the advent of powerful, low-cost computers and new algorithms. We describe a general-purpose implementation of the Bayesian methodology ...