This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation,” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. Loopy belief propagation, because it propagates exact belief states, is useful for a limi...

The expectation maximization (EM) algorithm computes maximum likelihood estimates of unknown parameters in probabilistic models involving latent variables. More pragmatically speaking, the EM algorithm is an iterative method that alternates between computing a conditional expectation and solving a maximization problem, hence the name expectation maximization. We will in this work derive the EM ...

BACKGROUND The expected value of partial perfect information (EVPPI) is a theoretically justifiable and informative measure of uncertainty in decision-analytic cost-effectiveness models, but its calculation is computationally intensive because it generally requires two-level Monte Carlo simulation. We introduce an efficient, one-level simulation method for the calculation of single-parameter EV...

The notion of a $D$-poset was introduced in a connection withquantum mechanical models. In this paper, we introduce theconditional expectation of  random variables on theK^{o}pka's $D$-Poset and prove the basic properties ofconditional expectation on this  structure.

Objective: Type 2 diabetes mellitus (T2DM) is a chronic condition that requires consistent medical care to help control glycemic indices. Diabetes self-management is found to be essential for optimal glycemic control. This study aimed to investigate the predictors of diabetes self-management in adult with T2DM. Materials and Methods: A cross-sectional study was conducted. A purposive sample of...

# 1999 International Union of Crystallography Printed in Denmark ± all rights reserved Solvent ̄attening is a powerful tool for improving crystallographic phases for macromolecular structures obtained at moderate resolution, but uncertainties in the optimal weighting of experimental phases and modi®ed phases make it dif®cult to extract all the phase information possible. Solvent ̄attening is esse...

In this paper, we present an efficient method for determining the solution of the stochastic second kind Volterra integral equations (SVIE) by using the Taylor expansion method. This method transforms the SVIE to a linear stochastic ordinary differential equation which needs specified boundary conditions. For determining boundary conditions, we use the integration technique. This technique give...