Bayesian Estimation of State-Space Models Using the Metropolis-Hastings Algorithm within Gibbs Sampling∗
نویسندگان
چکیده
In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state space modeling in a Bayesian framework, which corresponds to an extension of Carlin, Polson and Stoffer (1992) and Carter and Kohn (1994, 1996). Using the Gibbs sampler and the Metropolis-Hastings algorithm, an asymptotically exact estimate of the smoothing mean is obtained from any nonlinear and/or non-Gaussian model. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed Bayes estimator.
منابع مشابه
Approximating Bayes Estimates by Means of the Tierney Kadane, Importance Sampling and Metropolis-Hastings within Gibbs Methods in the Poisson-Exponential Distribution: A Comparative Study
Here, we work on the problem of point estimation of the parameters of the Poisson-exponential distribution through the Bayesian and maximum likelihood methods based on complete samples. The point Bayes estimates under the symmetric squared error loss (SEL) function are approximated using three methods, namely the Tierney Kadane approximation method, the importance sampling method and the Metrop...
متن کاملNonlinear state-space modeling of fisheries biomass dynamics using Metropolis-Hastings within Gibbs sampling
State-space modeling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state-space models that may be nonlinear and have non-normal errors, and hence it is particularly useful for the modeling of fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus product...
متن کاملBayesian Estimation of Parameters in the Exponentiated Gumbel Distribution
Abstract: The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EG's parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using ...
متن کاملBayesian Analysis of Spatial Probit Models in Wheat Waste Management Adoption
The purpose of this study was to identify factors influencing the adoption of wheat waste management by wheat farmers. The method used in this study using the spatial Probit models and Bayesian model was used to estimate the model. MATLAB software was used in this study. The data of 220 wheat farmers in Khouzestan Province based on random sampling were collected in winter 2016. To calculate Bay...
متن کاملLinear-Time Gibbs Sampling in Piecewise Graphical Models
Many real-world Bayesian inference problems such as preference learning or trader valuation modeling in financial markets naturally use piecewise likelihoods. Unfortunately, exact closed-form inference in the underlying Bayesian graphical models is intractable in the general case and existing approximation techniques provide few guarantees on both approximation quality and efficiency. While (Ma...
متن کامل