Table of Contents
Fetching ...

Ziv-Merhav estimation for hidden-Markov processes

Nicholas Barnfield, Raphaël Grondin, Gaia Pozzoli, Renaud Raquépas

TL;DR

A proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes using tools from the thermodynamic formalism is presented.

Abstract

We present a proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes. Our proof strategy has two novel aspects: the focus on decoupling properties of the laws and the use of tools from the thermodynamic formalism.

Ziv-Merhav estimation for hidden-Markov processes

TL;DR

A proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes using tools from the thermodynamic formalism is presented.

Abstract

We present a proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes. Our proof strategy has two novel aspects: the focus on decoupling properties of the laws and the use of tools from the thermodynamic formalism.
Paper Structure (12 sections, 8 theorems, 34 equations, 2 figures, 1 algorithm)

This paper contains 12 sections, 8 theorems, 34 equations, 2 figures, 1 algorithm.

Key Result

Theorem 2

Suppose that $\mathbf{X}$ and $\mathbf{Y}$ are independent, HMPs with respective laws $\mathbb{P}_{\mathbf{X}}$ and $\mathbb{P}_{\mathbf{Y}}$. If they both satisfy Conditions i--iii, then almost surely.

Figures (2)

  • Figure 1: The pressure may or may not be differentiable, but its left derivative at the origin can still be identified as the cross entropy rate under quite general assumptions. As a visual aid to the convexity argument for Lemma \ref{['lem:ing-IV']}, the gray dashed line (figuratively) has slope $D_-\bar{q}(0)-\tfrac{\epsilon}{4}$.
  • Figure 2: For single realizations of HMPs $\bf{X}$ and $\bf{Y}$, we plot different estimators of $h^{\textnormal{c}}(\mathbb{P}_{\mathbf{Y}}|\mathbb{P}_{\mathbf{X}})$ up to $N = 2^{20}$ (Top). Over 32 realizations, we plot our estimation of the RMSE of the different estimators up to $N = 2^{17}$ (Bottom).

Theorems & Definitions (15)

  • Definition 1
  • Theorem 2
  • Lemma 3
  • Lemma 4
  • Definition 5
  • Lemma 6
  • proof : Proof sketch
  • Lemma 7
  • proof : Proof sketch
  • Lemma 8
  • ...and 5 more