Abstract: The dimension and the complexity of inference problems have dramatically increased in statistical signal processing. It thus becomes mandatory to design improved proposal schemes in ...

In the early years of computational physics, starting during the Second World War, the discipline and its practitioners did not yet have a name. Iulia Georgescu tells the story of these forgotten ...

Abstract: Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding, and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in ...

This important study introduces a fully differentiable variant of the Gillespie algorithm as an approximate stochastic simulation scheme for complex chemical reaction networks, allowing kinetic ...

Includes estimation of various parametric and semiparametric mixtures-of-regressions models. Functions to help with determining the number of components, including bootstrapping the likelihood ratio ...

ABSTRACT: This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian ...