158 citations · 263 across the 7 of their papers we have counts for
8 papers
Gibbsian polar slice sampling
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…
Geodesic slice sampling on the sphere
Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar +1
Probability measures on the sphere form an important class of statistical models and are used, for example, in modeling directional data or shapes. Due to their widespread use, but…
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…
Stability of doubly-intractable distributions
Michael Habeck, Daniel Rudolf, Björn Sprungk
Doubly-intractable distributions appear naturally as posterior distributions in Bayesian inference frameworks whenever the likelihood contains a normalizing function . Having tw…
Ensemble annealing of complex physical systems
Michael Habeck
Algorithms for simulating complex physical systems or solving difficult optimization problems often resort to an annealing process. Rather than simulating the system at the tempera…
Bayesian Evidence and Model Selection
Kevin H. Knuth, Michael Habeck, Nabin K. Malakar +2
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along…
Adaptive nonparametric detection in cryo-electron microscopy
Mikhail Langovoy, Michael Habeck, Bernhard Schoelkopf
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-…
Spatial statistics, image analysis and percolation theory
Mikhail Langovoy, Michael Habeck, Bernhard Schölkopf
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically ad…