As the combination of parameter learning and structure learning, learning Bayesian networks can also be examined, Parameter learning is estimation of the dependencies in the network. Structural learning is the estimation of the links of the network. In terms of whether the structure of the network is known and whether the variables are all observable, there are four types of learning Bayesian n...

Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at d...

The estimation of Bayesian networks given high-dimensional data, in particular gene expression data, has been the focus of much recent research. Whilst there are several methods available for the estimation of such networks, these typically assume that the data consist of independent and identically distributed samples. It is often the case, however, that the available data have a more complex ...

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demand...

here in, an application of a new seismic inversion algorithm in one of iran’s oilfields is described. stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. this method integrates information from different data sources with different scales, as prior informat...

Prologue The goal of this tutorial presentation is to focus on the pervasiveness of Monte-Carlo integration and importance sampling in Bayesian estimation, in general, and in particle filtering, in particular. This tutorial is a continuation of my tutorial: " ML, MAP, and Bayesian — The Holy Trinity of Parameter Estimation and Data Prediction " that can be downloaded from:

In this contribution we describe an object{oriented software architecture for image segmentation, 3{D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identi cation applies the Bayesian decision rule. We show results of 3{D object recognition experiments based on the observation of 2{...

The complete final product of Bayesian inference is the posterior distribution of the quantity of interest. Important inference summaries include point estimation, region estimation, and precise hypotheses testing. Those summaries may appropriately be described as the solution to specific decision problems which depend on the loss function chosen. The use of a continuous loss function leads to ...

Models of glucose metabolism can help to simulate and predict the blood glucose response in hyperglycaemic, critically ill patients. Model prediction performance depends on a sufficiently accurate estimation of the patient’s time-varying insulin sensitivity. The work presents three least squares approaches, the integral method and a Bayesian method that have been compared by prediction accuracy...

Stochastic inverse problems in heat conduction with consideration of uncertainties in the measured temperature data, temperature sensor locations and thermophysical properties are addressed using a Bayesian statistical inference method. Both parameter estimation and thermal history reconstruction problems, including boundary heat flux and heat source reconstruction, are studied. Probabilistic s...