12,388 research outputs found
Inference with Constrained Hidden Markov Models in PRISM
A Hidden Markov Model (HMM) is a common statistical model which is widely
used for analysis of biological sequence data and other sequential phenomena.
In the present paper we show how HMMs can be extended with side-constraints and
present constraint solving techniques for efficient inference. Defining HMMs
with side-constraints in Constraint Logic Programming have advantages in terms
of more compact expression and pruning opportunities during inference.
We present a PRISM-based framework for extending HMMs with side-constraints
and show how well-known constraints such as cardinality and all different are
integrated. We experimentally validate our approach on the biologically
motivated problem of global pairwise alignment
Duality in Graphical Models
Graphical models have proven to be powerful tools for representing
high-dimensional systems of random variables. One example of such a model is
the undirected graph, in which lack of an edge represents conditional
independence between two random variables given the rest. Another example is
the bidirected graph, in which absence of edges encodes pairwise marginal
independence. Both of these classes of graphical models have been extensively
studied, and while they are considered to be dual to one another, except in a
few instances this duality has not been thoroughly investigated. In this paper,
we demonstrate how duality between undirected and bidirected models can be used
to transport results for one class of graphical models to the dual model in a
transparent manner. We proceed to apply this technique to extend previously
existing results as well as to prove new ones, in three important domains.
First, we discuss the pairwise and global Markov properties for undirected and
bidirected models, using the pseudographoid and reverse-pseudographoid rules
which are weaker conditions than the typically used intersection and
composition rules. Second, we investigate these pseudographoid and reverse
pseudographoid rules in the context of probability distributions, using the
concept of duality in the process. Duality allows us to quickly relate them to
the more familiar intersection and composition properties. Third and finally,
we apply the dualization method to understand the implications of faithfulness,
which in turn leads to a more general form of an existing result
Efficient CSL Model Checking Using Stratification
For continuous-time Markov chains, the model-checking problem with respect to
continuous-time stochastic logic (CSL) has been introduced and shown to be
decidable by Aziz, Sanwal, Singhal and Brayton in 1996. Their proof can be
turned into an approximation algorithm with worse than exponential complexity.
In 2000, Baier, Haverkort, Hermanns and Katoen presented an efficient
polynomial-time approximation algorithm for the sublogic in which only binary
until is allowed. In this paper, we propose such an efficient polynomial-time
approximation algorithm for full CSL. The key to our method is the notion of
stratified CTMCs with respect to the CSL property to be checked. On a
stratified CTMC, the probability to satisfy a CSL path formula can be
approximated by a transient analysis in polynomial time (using uniformization).
We present a measure-preserving, linear-time and -space transformation of any
CTMC into an equivalent, stratified one. This makes the present work the
centerpiece of a broadly applicable full CSL model checker. Recently, the
decision algorithm by Aziz et al. was shown to work only for stratified CTMCs.
As an additional contribution, our measure-preserving transformation can be
used to ensure the decidability for general CTMCs.Comment: 18 pages, preprint for LMCS. An extended abstract appeared in ICALP
201
Unprovability of the Logical Characterization of Bisimulation
We quickly review labelled Markov processes (LMP) and provide a
counterexample showing that in general measurable spaces, event bisimilarity
and state bisimilarity differ in LMP. This shows that the logic in Desharnais
[*] does not characterize state bisimulation in non-analytic measurable spaces.
Furthermore we show that, under current foundations of Mathematics, such
logical characterization is unprovable for spaces that are projections of a
coanalytic set. Underlying this construction there is a proof that stationary
Markov processes over general measurable spaces do not have semi-pullbacks.
([*] J. Desharnais, Labelled Markov Processes. School of Computer Science.
McGill University, Montr\'eal (1999))Comment: Extended introduction and comments; extra section on semi-pullbacks;
11 pages Some background details added; extra example on the non-locality of
state bisimilarity; 14 page
Understanding interdependency through complex information sharing
The interactions between three or more random variables are often nontrivial,
poorly understood, and yet, are paramount for future advances in fields such as
network information theory, neuroscience, genetics and many others. In this
work, we propose to analyze these interactions as different modes of
information sharing. Towards this end, we introduce a novel axiomatic framework
for decomposing the joint entropy, which characterizes the various ways in
which random variables can share information. The key contribution of our
framework is to distinguish between interdependencies where the information is
shared redundantly, and synergistic interdependencies where the sharing
structure exists in the whole but not between the parts. We show that our
axioms determine unique formulas for all the terms of the proposed
decomposition for a number of cases of interest. Moreover, we show how these
results can be applied to several network information theory problems,
providing a more intuitive understanding of their fundamental limits.Comment: 39 pages, 4 figure
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