1 code implementation • 29 Jan 2024 • Philip Schär, Michael Habeck, Daniel Rudolf
The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior.
1 code implementation • 8 Feb 2023 • Philip Schär, Michael Habeck, Daniel Rudolf
Polar slice sampling (Roberts & Rosenthal, 2002) is a Markov chain approach for approximate sampling of distributions that is difficult, if not impossible, to implement efficiently, but behaves provably well with respect to the dimension.
no code implementations • 6 Jan 2023 • Mareike Hasenpflug, Viacheslav Natarovskii, Daniel Rudolf
We discuss the well-definedness of elliptical slice sampling, a Markov chain approach for approximate sampling of posterior distributions introduced by Murray, Adams and MacKay 2010.
no code implementations • 7 May 2021 • Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk
For Bayesian learning, given likelihood function and Gaussian prior, the elliptical slice sampler, introduced by Murray, Adams and MacKay 2010, provides a tool for the construction of a Markov chain for approximate sampling of the underlying posterior distribution.
1 code implementation • 10 Mar 2021 • Laura Jula Vanegas, Benjamin Eltzner, Daniel Rudolf, Miroslav Dura, Stephan E. Lehnart, Axel Munk
We propose and investigate a hidden Markov model (HMM) for the analysis of dependent, aggregated, superimposed two-state signal recordings.
Methodology
