Equivalence of History and Generator Epsilon-Machines

11/18/2011
by   Nicholas F. Travers, et al.
0

Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though, an alternative generator definition was given: unifilar, edge-emitting hidden Markov models with probabilistically distinct states. The key difference is that history epsilon-machines are defined by a process, whereas generator epsilon-machines define a process. We show here that these two definitions are equivalent in the finite-state case.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/27/2020

Equivalence of Hidden Markov Models with Continuous Observations

We consider Hidden Markov Models that emit sequences of observations tha...
research
03/30/2018

Local Equivalence Problem in Hidden Markov Model

In the hidden Markovian process, there is a possibility that two differe...
research
08/01/2017

Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?

Understanding the generative mechanism of a natural system is a vital co...
research
05/16/2023

Can we forget how we learned? Representing states in iterated belief revision

The three most common representations of states in iterated belief revis...
research
12/06/2019

Oracular information and the second law of thermodynamics

Patterns and processes - spatial and temporal correlations - are subject...
research
11/06/2007

Infinite Viterbi alignments in the two state hidden Markov models

Since the early days of digital communication, Hidden Markov Models (HMM...
research
08/11/2022

Algebraic Reduction of Hidden Markov Models

The problem of reducing a Hidden Markov Model (HMM) to a one of smaller ...

Please sign up or login with your details

Forgot password? Click here to reset