In order to estimate model parameters and circumvent possible dif-6 ficulties encountered with the likelihood function, we propose to replace the like-7 lihood in the formula of the posterior distribution by a function depending on a 8 contrast. The properties of the contrast-based (CB) posterior distribution and 9 MAP estimator are studied to understand what the consequences of incorporat-10 i...

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then ...

The analysis of general dynamic models involves a sequence of posterior distributions corresponding to the subsequent stages of the dynamic model. In the absence of normal/linear structure numerical integration schemes are required to estimate features of these posterior distributions. This paper reviews some previously suggested Monte Carlo based algorithms and suggests a new scheme which make...

This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero exponentially fast on any δ-contraction outside the identified region. Inside, it is bounded below by a pos...

Modern Bayesian inference typically requires some form of posterior approximation, and mean-field variational inference (MFVI) is an increasingly popular choice due to its speed. But MFVI can be inaccurate in various aspects, including an inability to capture multimodality in the posterior and underestimation of the posterior covariance. These issues arise since MFVI considers approximations to...

Bayesian inference of phylogeny is unique among phylogenetic reconstruction methods in that it produces a posterior distribution of trees rather than a point estimate of the best tree. The most common way to summarize this distribution is to report the majority-rule consensus tree annotated with the marginal posterior probabilities of each partition. Reporting a single tree discards information...

Abstract. Open-source R language can implement quantitative research using the flexibility, adaptability and simplicity of bayesian inference models. Counts drought which are regarded as realizations “event” in a Poisson process follows an Gamma distribution (prior distribution) case study Yucheng city with support language. That is, annual counts City during 10 years from 1974 to 1983 is proce...

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then ...

Linear inverse Gaussian problems is traditionally solved using least squares based inversion. The center of the posterior Gaussian probability distribution is often chosen as the solution to such problems, while the solution is in fact the posterior Gaussian probability distribution itself. We present an algorithm, based on direct sequential simulation, which can be used to efficiently draw sam...

The use of the Bayesian tools in system identification and model updating paradigms has been increased in the last ten years. Usually, the Bayesian techniques can be implemented to incorporate the uncertainties associated with measurements as well as the prediction made by the finite element model (FEM) into the FEM updating procedure. In this case, the posterior distribution function describes...