Event Diary
CRiSM Seminar
Location: A1.01
Prof Donald Martin, North Carolina State University
Markov chain pattern distributions
We give a method for predicting statistics of hidden state sequences, where the conditional distribution of states given observations is modeled by a factor graph with factors that depend on past states but not future ones. Model structure is exploited to develop a deterministic finite automaton and an associated Markov chain that facilitates efficient computation of the distributions. Examples of applications of the methodology are the computation of distributions of patterns and statistics in a discrete hidden state sequence perturbed by noise and/or missing values, and patterns in a state sequence that serves to classify the observations. Two detailed examples are given to illustrate the computational procedure.
Markov chain pattern distributions
We give a method for predicting statistics of hidden state sequences, where the conditional distribution of states given observations is modeled by a factor graph with factors that depend on past states but not future ones. Model structure is exploited to develop a deterministic finite automaton and an associated Markov chain that facilitates efficient computation of the distributions. Examples of applications of the methodology are the computation of distributions of patterns and statistics in a discrete hidden state sequence perturbed by noise and/or missing values, and patterns in a state sequence that serves to classify the observations. Two detailed examples are given to illustrate the computational procedure.