Anthony N. Pettitt
Statistics and Probability · 2004–2016
A researcher in the Science corpus: 8 papers; trusts 46 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 43 citations into it; #1,460 of 12,932 by trust here, #830 by citations
- Artificial Intelligence — 32 citations into it; #6,394 of 35,345 by trust here, #8,157 by citations
- Probabilistic & Bayesian Methods — 26 citations into it
- Management Science and Operations Research — 7 citations into it; #8,166 of 11,946 by trust here, #8,469 by citations
- Applied Mathematics — 6 citations into it
- Astronomy and Astrophysics — 6 citations into it
- Computational Theory and Mathematics — 6 citations into it; #7,672 of 12,182 by trust here, #8,756 by citations
- Mathematical Physics — 6 citations into it; #1,875 of 8,562 by trust here, #1,610 by citations
Whom Anthony N. Pettitt trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter J. Green
- Andrew Gelman
- Simon J. Godsill
- Tim Hesterberg
- Julian Besag
- Radford M. Neal
- R. Reeves
- Gareth O. Roberts
- Xiao-Li Meng
- Nicolas Chopin
Who trusts Anthony N. Pettitt overall
- Nial Friel
- Chris J. Oates
- Samuel J. Gershman
- Peter J. Green
- Mark A. Girolami
- Anna Pajor
- Jesper Møller
- Ben Calderhead
- Peter Beerli
- Radford M. Neal
Papers from Anthony N. Pettitt's perspective
Ordered by how strongly the researchers Anthony N. Pettitt trusts endorse each paper. Same corpus, a different order for every researcher.
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Information Theory, Inference, and Learning Algorithms
- Spatial Interaction and the Statistical Analysis of Lattice Systems
- Exact sampling with coupled Markov chains and applications to statistical mechanics
- Monte Carlo strategies in scientific computing
- Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods
- Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling
- Bayesian measures of model complexity and fit
- Monte Carlo Strategies in Scientific Computing
- Optimal scaling of discrete approximations to Langevin diffusions
Their papers
- An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants (2006) — 410 citations
- Marginal likelihood estimation via power posteriors (2008) — 360 citations
- Approximate Bayesian computation using indirect inference (2011) — 109 citations
- Introduction to MCMC (2012) — 75 citations
- A Review of Modern Computational Algorithms for Bayesian Optimal Design (2016) — 359 citations
- Importance Nested Sampling and the MultiNest Algorithm (2013) — 693 citations
- Efficient recursions for general factorisable models (2004) — 76 citations
- Wiley Series in Probability and Statistics (2012) — 1,234 citations