Backward Induced Probability Models

Comparing of different normal tissue complication probability models for plan evaluation of radical radiotherapy for esophageal cancer

Introduction: Chemoradiation is a well-established protocol for management of localized esophageal cancer. However, some early and late toxicities are associated with this therapy. Plan evaluation in term of normal tissue complication probability (NTCP) may help clinicians to tailor this therapy more individually. Given the variety of present radiobiological models, the choice...

Inside-Outside Probability Computation for Belief Propagation

In this paper we prove that the well-known correspondence between the forward-backward algorithm for hidden Markov models (HMMs) and belief propagation (BP) applied to HMMs can be generalized to one between BP for junction trees and the generalized inside-outside probability computation for probabilistic logic programs applied to junction trees.

Hidden Markov Models in Spoken Language Processing

This is a report about Hidden Markov Models, a data structure used to model the probabilities of sequences, and the three algorithms associated with it. The algorithms are the forward algorithm and the Viterbi algorithm, both used to calculate the probability of a sequence, and the forward-backward algorithm, used to train a Hidden Markov Model on a set of sequences, raising the propabilites of...

A backward selection procedure for approximating a discrete probability distribution by decomposable models

LES/FMDF of premixed methane/air flow in a backward-facing step combustor

In the present study, a hybrid Eulerian-Lagrangian methodology is utilized for large eddy simulation (LES) of premixed fuel/air flow over a three-dimensional backward facing step (BFS). The fluid dynamic features are obtained by solving the Eulerian filtered compressible transport equations while the species are predicted by using the filtered mass density function method (FMDF).  Some scalar f...