Steve P. Brooks
Statistics and Probability · 2000–2012
A researcher in the Science corpus: 9 papers; trusts 41 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 66 citations into it; #2,362 of 12,932 by trust here, #1,314 by citations
- Artificial Intelligence — 43 citations into it; #9,811 of 35,345 by trust here, #9,170 by citations
- Probabilistic & Bayesian Methods — 38 citations into it; #978 of 1,525 by trust here, #558 by citations
- Ecology — 18 citations into it; #9,218 of 10,717 by trust here, #7,011 by citations
- Nature and Landscape Conservation — 10 citations into it; #6,040 of 6,941 by trust here, #4,376 by citations
- Ecological Modeling — 7 citations into it
- Molecular Biology — 7 citations into it
- Mathematical Physics — 5 citations into it; #4,495 of 8,562 by trust here, #1,097 by citations
Whom Steve P. Brooks trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter J. Green
- Andrew Gelman
- Matthew Stephens
- Gareth O. Roberts
- Simon J. Godsill
- Donald B. Rubin
- P. Stephen
- Radford M. Neal
- Ioannis Ntzoufras
- Jun S. Liu
Who trusts Steve P. Brooks overall
- Paul Fearnhead
- Michael Betancourt
- Lawrence M. Murray
- David I. Hastie
- Benjamin Letham
- Olivier Gimenez
- S. Sarkodie
- Michael Schaub
- Panagiotis Besbeas
- Andrew Gelman
Papers from Steve P. Brooks's perspective
Ordered by how strongly the researchers Steve P. Brooks trusts endorse each paper. Same corpus, a different order for every researcher.
- Sampling-Based Approaches to Calculating Marginal Densities
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Inference from Iterative Simulation Using Multiple Sequences
- Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods
- Sequential Monte Carlo samplers
- Bayesian measures of model complexity and fit
- Examples of Adaptive MCMC
- Monte Carlo Sampling Methods Using Markov Chains and Their Applications
- Computational and Inferential Difficulties with Mixture Posterior Distributions
- Estimating the Dimension of a Model
Their papers
- Nonparametric Convergence Assessment for MCMC Model Selection (2003) — 112 citations
- Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions (2003) — 325 citations
- Output Assessment for Monte Carlo Simulations via the Score Statistic (2006) — 27 citations
- Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012) — 1,845 citations
- On the Bayesian analysis of population size (2001) — 92 citations
- Bayesian Animal Survival Estimation (2000) — 153 citations
- Weak Identifiability in Models for Mark-Recapture-Recovery Data (2009) — 83 citations
- WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009) — 100 citations
- A Bayesian approach to combining animal abundance and demographic data (2004) — 177 citations