We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Abstract. We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative l...
Ayres C. Fan, John W. Fisher III, William M. Wells...
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an...
— We present an algorithm that probabilistically covers a bounded region of the state space of a nonlinear system with a sparse tree of feedback stabilized trajectories leading t...
Background: Answers to several fundamental questions in statistical genetics would ideally require knowledge of the ancestral pedigree and of the gene flow therein. A few examples...
Dario Gasbarra, Matti Pirinen, Mikko J. Sillanp&au...