5 citations · 6 across the 4 of their papers we have counts for
6 papers
A generalized Bayesian approach to multiple changepoint analysis
Yuhui Wang, Andrew M. Thomas, Michael Jauch
We introduce a generalized Bayesian method for multiple changepoint analysis with a loss function inspired by multinomial logistic regression. The method does not require a specifi…
Bayesian changepoint detection via logistic regression and the topological analysis of image series
Andrew M. Thomas, Michael Jauch, David S. Matteson
We present a Bayesian method for multivariate changepoint detection that allows for simultaneous inference on the location of a changepoint and the coefficients of a logistic regre…
Mixture representations and Bayesian nonparametric inference for likelihood ratio ordered distributions
Michael Jauch, Andrés F. Barrientos, Víctor Peña +1
In this article, we introduce mixture representations for likelihood ratio ordered distributions. Essentially, the ratio of two probability densities, or mass functions, is monoton…
Monte Carlo simulation on the Stiefel manifold via polar expansion
Michael Jauch, Peter D. Hoff, David B. Dunson
Motivated by applications to Bayesian inference for statistical models with orthogonal matrix parameters, we present a general approach to Monte Carlo s…
Random orthogonal matrices and the Cayley transform
Michael Jauch, Peter D. Hoff, David B. Dunson
Random orthogonal matrices play an important role in probability and statistics, arising in multivariate analysis, directional statistics, and models of physical systems, among oth…
Bayesian Optimization with Shape Constraints
Michael Jauch, Víctor Peña
In typical applications of Bayesian optimization, minimal assumptions are made about the objective function being optimized. This is true even when researchers have prior informati…