An Introduction to Hidden Markov Models and Bayesian Networks

Introduction to Graphical Models, Hidden Markov Models and Bayesian Networks

1 History and Theoretical Basics of Hidden Markov Models Guy Leonard

The following chapter can be understood as one sort of brief introduction to the history and basics of the Hidden Markov Models. Hidden Markov Models (HMMs) are learnable finite stochastic automates. Nowadays, they are considered as a specific form of dynamic Bayesian networks. Dynamic Bayesian networks are based on the theory of Bayes (Bayes & Price, 1763). A Hidden Markov Model consists of tw...

A Bayesian Networks Approach to Reliability Analysis of a Launch Vehicle Liquid Propellant Engine

This paper presents an extension of Bayesian networks (BN) applied to reliability analysis of an open gas generator cycle Liquid propellant engine (OGLE) of launch vehicles. There are several methods for system reliability analysis such as RBD, FTA, FMEA, Markov Chains, and etc. But for complex systems such as LV, they are not all efficiently applicable due to failure dependencies between compo...

An Introduction to Bayesian Networks for Automatic Speech Recog

Bayesian Networks are a particular type of Graphical Models, providing a general and flexible framework to model, factor, and compute joint probability distributions among random variables in a compact and efficient way. For speech recognition, a BN permits each speech frame to be associated with an arbitrary set of random variables. They can be used to augment well-known statistical paradigms ...

Introducing Busy Customer Portfolio Using Hidden Markov Model

Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...