## Bayesian Statistics 9

*José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West (eds)*

- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199694587
- eISBN:
- 9780191731921
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199694587.001.0001
- Subject:
- Mathematics, Probability / Statistics

The Valencia International Meetings on Bayesian Statistics – established in 1979 and held every four years – have been the forum for a definitive overview of current concerns and activities in ... More

## Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

*Ludwig Fahrmeir and Thomas Kneib*

- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199533022
- eISBN:
- 9780191728501
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199533022.001.0001
- Subject:
- Mathematics, Probability / Statistics, Biostatistics

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) ... More

## Markov Chain Monte Carlo

*N. Thompson Hobbs and Mevin B. Hooten*

### in Bayesian Models: A Statistical Primer for Ecologists

- Published in print:
- 2015
- Published Online:
- October 2017
- ISBN:
- 9780691159287
- eISBN:
- 9781400866557
- Item type:
- chapter

- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691159287.003.0007
- Subject:
- Biology, Ecology

This chapter explains how to implement Bayesian analyses using the Markov chain Monte Carlo (MCMC) algorithm, a set of methods for Bayesian analysis made popular by the seminal paper of Gelfand and ... More

## Inference from a Single Model

*N. Thompson Hobbs and Mevin B. Hooten*

### in Bayesian Models: A Statistical Primer for Ecologists

- Published in print:
- 2015
- Published Online:
- October 2017
- ISBN:
- 9780691159287
- eISBN:
- 9781400866557
- Item type:
- chapter

- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691159287.003.0008
- Subject:
- Biology, Ecology

This chapter shows how to make inferences using MCMC samples. Here, the process of inference begins on the assumption that a single model is being analyzed. The objective is to estimate parameters, ... More

## Parsimony and Bayesian phylogenetics

*Pablo A. Goloboff and Diego Pol*

### in Parsimony, Phylogeny, and Genomics

- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780199297306
- eISBN:
- 9780191713729
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199297306.003.0008
- Subject:
- Biology, Evolutionary Biology / Genetics

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method ... More

## Inference

*Jesper Møller*

### in New Perspectives in Stochastic Geometry

- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780199232574
- eISBN:
- 9780191716393
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199232574.003.0009
- Subject:
- Mathematics, Geometry / Topology

This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based ... More

## Advances in Markov chain Monte Carlo

*Griffin Jim E and Stephens David A*

### in Bayesian Theory and Applications

- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199695607
- eISBN:
- 9780191744167
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199695607.003.0007
- Subject:
- Mathematics, Probability / Statistics

This chapter traces some of the key developments that further developed the underpinning theory and potential applications of Markov chain Monte Carlo (MCMC) since the mid 1990s. In particular, it ... More

## Molecular Evolution: A Statistical Approach

*Ziheng Yang*

- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics

This book summarizes the statistical models and computational algorithms for comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, and ... More

## Bridges: Inference and the Monte Carlo method

*Marc Mézard and Andrea Montanari*

### in Information, Physics, and Computation

- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780198570837
- eISBN:
- 9780191718755
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198570837.003.0013
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics

The mathematical structure highlighted in this chapter by the factor graph representation is the locality of probabilistic dependencies between variables. Locality also emerges in many problems of ... More

## Bayesian Models for Sparse Regression Analysis of High Dimensional Data *

*Sylvia Richardson, Leonardo Bottolo, and Jeffrey S. Rosenthal*

### in Bayesian Statistics 9

- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199694587
- eISBN:
- 9780191731921
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199694587.003.0018
- Subject:
- Mathematics, Probability / Statistics

This paper considers the task of building efficient regression models for sparse multivariate analysis of high dimensional data sets, in particular it focuses on cases where the numbers q of ... More

## Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology

*Darren J. Wilkinson*

### in Bayesian Statistics 9

- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199694587
- eISBN:
- 9780191731921
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199694587.003.0023
- Subject:
- Mathematics, Probability / Statistics

Bacteria are single‐celled organisms which often display heterogeneous behaviour, even among populations of genetically identical cells in uniform environmental conditions. Markov process models ... More

## Bayesian Variable Selection for Random Intercept Modeling of Gaussian and Non‐Gaussian Data

*Sylvia Frühwirth‐Schnatter and Helga Wagner*

### in Bayesian Statistics 9

- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199694587
- eISBN:
- 9780191731921
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199694587.003.0006
- Subject:
- Mathematics, Probability / Statistics

The paper demonstrates that Bayesian variable selection for random intercept models is closely related to the appropriate choice of the distribution of heterogeneity. If, for instance, a Laplace ... More

## Sequential Monte Carlo Methods

*Edward P. Herbst and Frank Schorfheide*

### in Bayesian Estimation of DSGE Models

- Published in print:
- 2015
- Published Online:
- October 2017
- ISBN:
- 9780691161082
- eISBN:
- 9781400873739
- Item type:
- chapter

- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691161082.003.0005
- Subject:
- Economics and Finance, Econometrics

This chapter analyzes Sequential Monte Carlo (SMC) algorithms and how they were initially developed to solve filtering problems that arise in nonlinear state–space models. The first paper that ... More

## Combining Particle Filters with MH Samplers

*Edward P. Herbst and Frank Schorfheide*

### in Bayesian Estimation of DSGE Models

- Published in print:
- 2015
- Published Online:
- October 2017
- ISBN:
- 9780691161082
- eISBN:
- 9781400873739
- Item type:
- chapter

- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691161082.003.0009
- Subject:
- Economics and Finance, Econometrics

This chapter argues that in order to conduct Bayesian inference, the approximate likelihood function has to be embedded into a posterior sampler. It begins by combining the particle filtering methods ... More

## Bayesian Theory and Applications

*Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens (eds)*

- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199695607
- eISBN:
- 9780191744167
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199695607.001.0001
- Subject:
- Mathematics, Probability / Statistics

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances ... More

## Markov chain Monte Carlo methods

*Chib Siddhartha*

### in Bayesian Theory and Applications

- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199695607
- eISBN:
- 9780191744167
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199695607.003.0006
- Subject:
- Mathematics, Probability / Statistics

This chapter provides a brief summary of Markov chain Monte Carlo (MCMC) methods. The chapter is organized as follows. Section 6.2 describes the Metropolis–Hastings algorithm and its generalized ... More

## Bayesian computation (MCMC)

*Ziheng Yang*

### in Molecular Evolution: A Statistical Approach

- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.003.0007
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics

This chapter provides a detailed introduction to modern Bayesian computation. The Metropolis–Hastings algorithm is illustrated using a simple example of distance estimation between two sequences. A ... More

## Bayesian phylogenetics

*Ziheng Yang*

### in Molecular Evolution: A Statistical Approach

- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.003.0008
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics

This chapter discusses the implementation of various models of genetic sequence evolution in Bayesian phylogenetic analysis. It discusses the specification of priors for parameters in such models, as ... More

## Coalescent theory and species trees

*Ziheng Yang*

### in Molecular Evolution: A Statistical Approach

- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.003.0009
- Subject:

This chapter introduces Kingman’s coalescent process, which describes the genealogical relationships within a sample of DNA sequences taken from a population, and forms the basis for likelihood-based ... More

## Missing data: mechanisms, methods, and messages

*Shinichi Nakagawa*

### in Ecological Statistics: Contemporary theory and application

- Published in print:
- 2015
- Published Online:
- April 2015
- ISBN:
- 9780199672547
- eISBN:
- 9780191796487
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199672547.003.0005
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology

Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, ... More