... in IHCPs and using this information as a leverage to solve the inverse problem. We adopted a Bayesian inference framework for the subsequent parameter estimation problem and developed a Gibbs sampling algorithm to sample from the posterior distribution of the model's parameters, enhancing the estimation...

... , New York . [9] Gelman , A. , Carlin , J. B. , Stern , H. S. , and Rubin , D. B. , 2004 , Bayesian Data Analysis , Chapman & Hall/CRC Press , Boca Raton , FL. [10] Gamerman , D. , 2002 , Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference...

... communities. Such methods have been widely used to deal with sequential Bayesian inference problems in the fields like economics, signal processing, and robotics, among others. SMC methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named...

...Carolina P. Naveira-Cotta; Helcio R. B. Orlande; Renato M. Cotta This work presents the combined use of the integral transform method, for the direct problem solution, and of Bayesian inference, for the inverse problem analysis, in the simultaneous estimation of spatially variable thermal...