We investigate branching properties of the solution of a fragmentation equation for the mass distribution and we properly associate a continuous time càdlàg Markov process on the space S↓ of all fragmentation sizes, introduced by J. Bertoin. A binary fragmentation kernel induces a specific...

This paper develops an asymptotic expansion technique in momentum space for stochastic filtering. It is shown that Fourier transformation combined with a polynomial-function approximation of the nonlinear terms gives a closed recursive system of ordinary differential equations (ODEs) for the...

In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of...

Abstract In this paper, we consider the Bayesian inference of M/M/𝑅 queue with 𝑅 heterogeneous servers with service rates \mu_{1},\mu_{2},\ldots,\mu_{R} , where \mu_{1}\mu_{2}\cdots\mu_{R} . Assuming multivariate gamma prior distribution for service rates and gamma prior distribution for...

In this paper, we offer the Gibbs sampler as an alternative to the GMM estimator developed by Berry, Levinsohn, and Pakes (Econometrica 63(4), 841–890, 1995) in their equilibrium differentiated product market analysis of the automobile industry. We use the GMM objective as the basis for...

A new technique for nonlinear state and parameter estimation of the discrete time stochastic volatility models in the state space form is developed. The Gibbs sampler is used to construct a Markov-chain simulation tool that reflects both inherent model variability and parameter uncertainty. The...