This thesis makes three main contributions to the literature on Dynamic Stochastic General Equilibrium (DSGE) models in Macroeconomics. As no previous studies have studied the Chinese economy from the perspective of DSGE, the first contribution of this thesis is estimating a DSGE model for China through a Bayesian approach using the Chinese quarterly post-economic reform data representing the m...

This is an expository paper dealing with Bayesian inference for three important mixture problems in the area of quality and reliability. The traditional approach for estimation in these situations is the method of maximum likelihood. The corresponding inference based on large-sample theory can, however, be misleading in situations where the likelihood cannot be well approximated by the normal d...

Concentration ratios (CRs) are used to derive activity concentrations in wild plants and animals. Usually, compilations of CR values encompass a wide range of element-organism combinations, extracted from different studies with statistical information reported at varying degrees of detail. To produce a more robust estimation of distribution parameters, data from different studies are normally p...

background often, there is no access to sufficient sample size to estimate the prevalence using the method of direct estimator in all areas. the aim of this study was to compare small area’s bayesian method and direct method in estimating the prevalence of steatosis in obese and overweight children. materials and methods: in this cross-sectional study, was conducted on 150 overweight and obese ...

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarch...

Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing coclustering algorithms are hard partitioning, assigning each row and column of the data matrix to one cluster. Recently a Bayesian co-clustering approach has been proposed which allows a probability distribution membership in row and column clusters. The approach uses variatio...

We present mathematical and conceptual foundations for the task of robust amplitude estimation using engineered likelihood functions (ELFs), a framework introduced by Wang et al. [PRX Quantum 2, 010346 (2021)] that uses Bayesian inference to enhance rate information gain in quantum sampling. These ELFs, which are obtained choosing tunable parameters parametrized circuit minimize expected poster...

In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy time delay estimations. The existing methods uniform discrete grid (signal sampling rate). When a true out grid, deteriorates due to mismatch between real-time grid. This paper proposes new method for estimation, which realizes under framework variational Bayesian inference. proposed grid-les...