We review three algorithms for parameter estimation of the Latent Dirichlet Allocation model: batch variational Bayesian inference, online variational Bayesian inference and inference using collapsed Gibbs sampling. We experimentally compare their time complexity and performance. We find that the online variational Bayesian inference converges faster than the other two inference techniques, wit...

We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed have excellent comparative performance scale well to very large sample sizes due a binning strategy. Moreover, approach is fully...

Abstract – A particle filter is applied to the problem of detecting and tracking multiple sound sources by Bayesian inference using combined audio and video information. The problem is formulated within a general framework of Bayesian hidden variable sequence estimation by fusing observed information. The particle filter is then introduced as an approximation of Bayesian inference. Experiments ...

We consider classical and Bayesian estimation procedures implemented by means of a set of conditional moment conditions that depend on latent variables. The latent variables evolve according to a Markovian transition density. Two main classes of applications are: 1) GMM estimation with time-varying parameters; and 2) estimation of nonlinear Dynamic Stochastic General Equilibrium (DSGE) models. ...

In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum ...

Heterogeneous data sets arise naturally in most applications due to the use of a variety of sensors, and measuring platforms. Such data sets can be heterogeneous in terms of the error characteristics, and sensor models. Treating such data is most naturally accomplished using a Bayesian or model based geostatistical approach, however such methods generally scale rather badly with the size of dat...

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into subsets, analyze each subset using a Bayesian ...