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. ...

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven metho...

Demodulation of received signals for a known channel parameters over Rayleigh fading channel is done by BEM (Bayesian Estimation Maximization) algorithm using MAP (Maximum A Posteriori) Probability decisions. Simulation results were produced using this demodulator for the specified mobile satellite based trans-receiver system to find the BER (Bit Error Rate). Comparisons are made to that of a Q...

Structural equation models (SEM) are frequently used in Information Systems (IS) to analyze and test theoretical propositions. As IS researchers frequently reuse measurement instruments and adapt or extend theories, it is not uncommon for a researcher to re-estimate regression relationships in their SEM that have been examined in previous studies. We advocate the use of Bayesian estimation of s...

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 ...

This paper presents Bayesian approaches to parameter estimation in the log-linear analysis of sparse frequency tables. The proposed methods overcome the non-estimability problems that may occur when applying maximum likelihood estimation. A crucial point when using Bayesian methods is the specification of the prior distributions for the model parameters. We discuss the various possible priors a...

A Bayesian analysis is used to detect a change-point in a sequence of independent random variables from exponential distributions. In This paper, we try to estimate change point which occurs in any sequence of independent exponential observations. The Bayes estimators are derived for change point, the rate of exponential distribution before shift and the rate of exponential distribution after s...

Bayesian estimation enables the individual pharmacokinetic parameters of the medication administrated to be estimated using only a few blood concentrations. Due to wide inter-individual variability in the pharmacokinetics of methotrexate (MTX), the concentration of MTX needs to be frequently determined during high-dose MTX therapy in order to prevent toxic adverse events. To apply the benefits ...

AbsfrnctImage reconstruction in Bayesian framework is far more advantageous over other reconstruction methods like convolution back projection, weighted least square method and maximum likelihood estimation. The power of Bayesian estimation lies in its ability to incorporate the prior distribution knowledge, enabling better reconstruction. Proper specification of clique potentials in Bayesian e...