Ben Goodrich

Statistics and Probability · with Gabry · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 22 citations into it; #11,657 of 12,932 by trust here, #988 by citations
• Artificial Intelligence — 16 citations into it; #27,440 of 35,345 by trust here, #6,266 by citations
• Probabilistic & Bayesian Methods — 10 citations into it; #1,304 of 1,525 by trust here, #259 by citations

Whom Ben Goodrich trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Andrew Gelman
2. Radford M. Neal
3. Jonah Gabry
4. Michael Betancourt
5. Douglas M. Bates

Who trusts Ben Goodrich overall

1. Paul-Christian Burkner
2. Aki Vehtari
3. Andrew Gelman
4. Dominique Makowski
5. Edgar C. Merkle
6. David Meng-Chuen Chen
7. Matthew Lamm
8. Jonah Gabry
9. Yves Rosseel

Papers from Ben Goodrich's perspective

Ordered by how strongly the researchers Ben Goodrich trusts endorse each paper. Same corpus, a different order for every researcher.

1. Handbook of Markov Chain Monte Carlo
2. Fitting Linear Mixed-Effects Models Using lme4
3. Inference from Iterative Simulation Using Multiple Sequences
4. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
5. A General Metric for Riemannian Manifold Hamiltonian Monte Carlo
6. A method for solving the convex programming problem with convergence rate O(1/k^2)
7. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility (Statistics and computing)
8. Bayesian Applied Regression Modeling via Stan
9. Understanding predictive information criteria for Bayesian models (Statistics and computing)
10. Slice Sampling

Their papers

• Stan: A Probabilistic Programming Language (2017) — 7,506 citations
• R-squared for Bayesian Regression Models (2019) — 791 citations
• Bayesian Applied Regression Modeling via Stan (2016) — 858 citations