465 citations · 543 across the 10 of their papers we have counts for
16 papers
A Short Review of Ergodicity and Convergence of Markov chain Monte Carlo Estimators
Michael Betancourt
This short note reviews the basic theory for quantifying both the asymptotic and preasymptotic convergence of Markov chain Monte Carlo estimators.
A Unifying and Canonical Description of Measure-Preserving Diffusions
Alessandro Barp, So Takao, Michael Betancourt +2
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework. In this paper, we develop a geo…
Workflow Techniques for the Robust Use of Bayes Factors
Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner +2
Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed…
The Discrete Adjoint Method: Efficient Derivatives for Functions of Discrete Sequences
Michael Betancourt, Charles C. Margossian, Vianey Leos-Barajas
Gradient-based techniques are becoming increasingly critical in quantitative fields, notably in statistics and computer science. The utility of these techniques, however, ultimatel…
Incomplete Reparameterizations and Equivalent Metrics
Michael Betancourt
Reparameterizing a probabilisitic system is common advice for improving the performance of a statistical algorithm like Markov chain Monte Carlo, even though in theory such reparam…
Toward a principled Bayesian workflow in cognitive science
Daniel J. Schad, Michael Betancourt, Shravan Vasishth
Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the develo…