The key task in probabilistic reasoning is to appropriately update one’s beliefs as one obtains new information in the form of evidence. In many application settings, however, th...
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely o...
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreir...
We consider Bayesian detection/classification of discrete random parameters that are strongly dependent locally due to some deterministic local constraint. Based on the recently ...
Georg Kail, Jean-Yves Tourneret, Franz Hlawatsch, ...
Abstract. In this paper we consider the problem of computing the density of states of a Boolean formula in CNF, a generalization of both MAX-SAT and model counting. Given a Boolean...
—Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyze the res...
Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J...
Reversible jump Markov chain Monte Carlo (RJMCMC) is a recent method which makes it possible to construct reversible Markov chain samplers that jump between parameter subspaces of...
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-i...
Yangyang Zhang, Chunlin Ji, Yi Liu, Wasim Q. Malik...
Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations...