20,995 research outputs found
Phase Transitions in Nonlinear Filtering
It has been established under very general conditions that the ergodic
properties of Markov processes are inherited by their conditional distributions
given partial information. While the existing theory provides a rather complete
picture of classical filtering models, many infinite-dimensional problems are
outside its scope. Far from being a technical issue, the infinite-dimensional
setting gives rise to surprising phenomena and new questions in filtering
theory. The aim of this paper is to discuss some elementary examples,
conjectures, and general theory that arise in this setting, and to highlight
connections with problems in statistical mechanics and ergodic theory. In
particular, we exhibit a simple example of a uniformly ergodic model in which
ergodicity of the filter undergoes a phase transition, and we develop some
qualitative understanding as to when such phenomena can and cannot occur. We
also discuss closely related problems in the setting of conditional Markov
random fields.Comment: 51 page
Infinite Hidden Conditional Random Fields for the Recognition of Human Behaviour
While detecting and interpreting temporal patterns of nonverbal behavioral cues
in a given context is a natural and often unconscious process for humans, it
remains a rather difficult task for computer systems.
In this thesis we are primarily motivated by the problem of recognizing
expressions of high--level behavior, and specifically agreement and
disagreement.
We thoroughly dissect the problem by surveying the nonverbal behavioral cues
that could be present during displays of agreement and disagreement; we discuss
a number of methods that could be used or adapted to detect these suggested
cues; we list some publicly available databases these tools could be trained on
for the analysis of spontaneous, audiovisual instances of agreement and
disagreement, we examine the few existing attempts at agreement and disagreement
classification, and we discuss the challenges in automatically detecting
agreement and disagreement.
We present
experiments that show that an existing discriminative graphical model, the
Hidden Conditional Random Field (HCRF) is the best performing on this task. The
HCRF is a discriminative latent variable model which has been previously shown
to successfully learn the hidden structure of a given classification problem
(provided an appropriate validation of the number of hidden states).
We show here that HCRFs are also able to capture what makes each of these social
attitudes unique. We present an efficient technique to analyze the concepts
learned by the HCRF model and show that these coincide with the findings from
social psychology regarding which cues are most prevalent in agreement and
disagreement. Our experiments are performed on a spontaneous expressions dataset
curated from real televised debates.
The HCRF model outperforms conventional approaches such as Hidden Markov Models
and Support Vector Machines.
Subsequently, we examine existing graphical models that use Bayesian
nonparametrics to have a countably infinite number of hidden states and adapt
their complexity to the data at hand.
We identify a gap in the literature that is the lack of a discriminative such
graphical model and we present our suggestion for the first such model: an HCRF
with an infinite number of hidden states, the Infinite Hidden Conditional Random
Field (IHCRF).
In summary, the IHCRF is an undirected discriminative graphical model for
sequence classification and uses a countably infinite number of hidden states.
We present two variants of this model. The first is a fully nonparametric model
that relies on Hierarchical Dirichlet Processes and a Markov Chain Monte Carlo
inference approach. The second is a semi--parametric model that uses Dirichlet
Process Mixtures and relies on a mean--field variational inference approach. We
show that both models are able to converge to a correct number of represented
hidden states, and perform as well as the best finite HCRFs ---chosen via
cross--validation--- for the difficult tasks of recognizing instances of
agreement, disagreement, and pain in audiovisual sequences.Open Acces
Conditional ergodicity in infinite dimension
The goal of this paper is to develop a general method to establish
conditional ergodicity of infinite-dimensional Markov chains. Given a Markov
chain in a product space, we aim to understand the ergodic properties of its
conditional distributions given one of the components. Such questions play a
fundamental role in the ergodic theory of nonlinear filters. In the setting of
Harris chains, conditional ergodicity has been established under general
nondegeneracy assumptions. Unfortunately, Markov chains in infinite-dimensional
state spaces are rarely amenable to the classical theory of Harris chains due
to the singularity of their transition probabilities, while topological and
functional methods that have been developed in the ergodic theory of
infinite-dimensional Markov chains are not well suited to the investigation of
conditional distributions. We must therefore develop new measure-theoretic
tools in the ergodic theory of Markov chains that enable the investigation of
conditional ergodicity for infinite dimensional or weak-* ergodic processes. To
this end, we first develop local counterparts of zero-two laws that arise in
the theory of Harris chains. These results give rise to ergodic theorems for
Markov chains that admit asymptotic couplings or that are locally mixing in the
sense of H. F\"{o}llmer, and to a non-Markovian ergodic theorem for stationary
absolutely regular sequences. We proceed to show that local ergodicity is
inherited by conditioning on a nondegenerate observation process. This is used
to prove stability and unique ergodicity of the nonlinear filter. Finally, we
show that our abstract results can be applied to infinite-dimensional Markov
processes that arise in several settings, including dissipative stochastic
partial differential equations, stochastic spin systems and stochastic
differential delay equations.Comment: Published in at http://dx.doi.org/10.1214/13-AOP879 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A Tutorial on Bayesian Nonparametric Models
A key problem in statistical modeling is model selection, how to choose a
model at an appropriate level of complexity. This problem appears in many
settings, most prominently in choosing the number ofclusters in mixture models
or the number of factors in factor analysis. In this tutorial we describe
Bayesian nonparametric methods, a class of methods that side-steps this issue
by allowing the data to determine the complexity of the model. This tutorial is
a high-level introduction to Bayesian nonparametric methods and contains
several examples of their application.Comment: 28 pages, 8 figure
The Entropy of a Binary Hidden Markov Process
The entropy of a binary symmetric Hidden Markov Process is calculated as an
expansion in the noise parameter epsilon. We map the problem onto a
one-dimensional Ising model in a large field of random signs and calculate the
expansion coefficients up to second order in epsilon. Using a conjecture we
extend the calculation to 11th order and discuss the convergence of the
resulting series
Many Roads to Synchrony: Natural Time Scales and Their Algorithms
We consider two important time scales---the Markov and cryptic orders---that
monitor how an observer synchronizes to a finitary stochastic process. We show
how to compute these orders exactly and that they are most efficiently
calculated from the epsilon-machine, a process's minimal unifilar model.
Surprisingly, though the Markov order is a basic concept from stochastic
process theory, it is not a probabilistic property of a process. Rather, it is
a topological property and, moreover, it is not computable from any
finite-state model other than the epsilon-machine. Via an exhaustive survey, we
close by demonstrating that infinite Markov and infinite cryptic orders are a
dominant feature in the space of finite-memory processes. We draw out the roles
played in statistical mechanical spin systems by these two complementary length
scales.Comment: 17 pages, 16 figures:
http://cse.ucdavis.edu/~cmg/compmech/pubs/kro.htm. Santa Fe Institute Working
Paper 10-11-02
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