6 research outputs found
Fully simplified multivariate normal updates in non-conjugate variational message passing
Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference schemes are obtained. The simplicity of these expressions means that the updates can be achieved very eficiently. Since the Multivariate Normal family is the most common for approximating the joint posterior density function of a continuous parameter vector, these fully simplified updates are of great practical benefit. © 2014 Matt P. Wand
Variational Bayes with Intractable Likelihood
Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian
inference in statistical modeling. However, the existing VB algorithms are
restricted to cases where the likelihood is tractable, which precludes the use
of VB in many interesting situations such as in state space models and in
approximate Bayesian computation (ABC), where application of VB methods was
previously impossible. This paper extends the scope of application of VB to
cases where the likelihood is intractable, but can be estimated unbiasedly. The
proposed VB method therefore makes it possible to carry out Bayesian inference
in many statistical applications, including state space models and ABC. The
method is generic in the sense that it can be applied to almost all statistical
models without requiring too much model-based derivation, which is a drawback
of many existing VB algorithms. We also show how the proposed method can be
used to obtain highly accurate VB approximations of marginal posterior
distributions.Comment: 40 pages, 6 figure
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
Variational Bayes (VB) methods have emerged as a fast and
computationally-efficient alternative to Markov chain Monte Carlo (MCMC)
methods for scalable Bayesian estimation of mixed multinomial logit (MMNL)
models. It has been established that VB is substantially faster than MCMC at
practically no compromises in predictive accuracy. In this paper, we address
two critical gaps concerning the usage and understanding of VB for MMNL. First,
extant VB methods are limited to utility specifications involving only
individual-specific taste parameters. Second, the finite-sample properties of
VB estimators and the relative performance of VB, MCMC and maximum simulated
likelihood estimation (MSLE) are not known. To address the former, this study
extends several VB methods for MMNL to admit utility specifications including
both fixed and random utility parameters. To address the latter, we conduct an
extensive simulation-based evaluation to benchmark the extended VB methods
against MCMC and MSLE in terms of estimation times, parameter recovery and
predictive accuracy. The results suggest that all VB variants with the
exception of the ones relying on an alternative variational lower bound
constructed with the help of the modified Jensen's inequality perform as well
as MCMC and MSLE at prediction and parameter recovery. In particular, VB with
nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta)
is up to 16 times faster than MCMC and MSLE. Thus, VB-NCVMP-Delta can be an
attractive alternative to MCMC and MSLE for fast, scalable and accurate
estimation of MMNL models
Proceedings of the 36th International Workshop Statistical Modelling July 18-22, 2022 - Trieste, Italy
The 36th International Workshop on Statistical Modelling (IWSM) is the first one held in presence after a two year hiatus due to the COVID-19 pandemic.
This edition was quite lively, with 60 oral presentations and 53 posters, covering a vast variety of topics.
As usual, the extended abstracts of the papers are collected in the IWSM proceedings, but unlike the previous workshops, this year the proceedings will be not printed on paper, but it is only online.
The workshop proudly maintains its almost unique feature of scheduling one plenary session for the whole week. This choice has always contributed to the stimulating atmosphere of the conference, combined with its informal character, encouraging the exchange of ideas and cross-fertilization among different areas as a distinguished tradition of the workshop, student participation has been strongly encouraged. This IWSM edition is particularly successful in this respect, as testified by the large number of students included in the program