24,314 research outputs found
Quasi Conjunction, Quasi Disjunction, T-norms and T-conorms: Probabilistic Aspects
We make a probabilistic analysis related to some inference rules which play
an important role in nonmonotonic reasoning. In a coherence-based setting, we
study the extensions of a probability assessment defined on conditional
events to their quasi conjunction, and by exploiting duality, to their quasi
disjunction. The lower and upper bounds coincide with some well known t-norms
and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their
dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are
rules for which any finite family of conditional events p-entails the
associated quasi conjunction and quasi disjunction. We examine some cases of
logical dependencies, and we study the relations among coherence, inclusion for
conditional events, and p-entailment. We also consider the Or rule, where quasi
conjunction and quasi disjunction of premises coincide with the conclusion. We
analyze further aspects of quasi conjunction and quasi disjunction, by
computing probabilistic bounds on premises from bounds on conclusions. Finally,
we consider biconditional events, and we introduce the notion of an
-conditional event. Then we give a probabilistic interpretation for a
generalized Loop rule. In an appendix we provide explicit expressions for the
Hamacher t-norm and t-conorm in the unitary hypercube
On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of
statistical models, used in a wide variety of applications. Popular instances
of this class include Gaussian graphical models and Ising models. In many
settings, however, it might not be clear which subclass of graphical models to
use, particularly for non-Gaussian and non-categorical data. In this paper, we
consider a general sub-class of graphical models where the node-wise
conditional distributions arise from exponential families. This allows us to
derive multivariate graphical model distributions from univariate exponential
family distributions, such as the Poisson, negative binomial, and exponential
distributions. Our key contributions include a class of M-estimators to fit
these graphical model distributions; and rigorous statistical analysis showing
that these M-estimators recover the true graphical model structure exactly,
with high probability. We provide examples of genomic and proteomic networks
learned via instances of our class of graphical models derived from Poisson and
exponential distributions.Comment: Journal of Machine Learning Researc
Upwards Closed Dependencies in Team Semantics
We prove that adding upwards closed first-order dependency atoms to
first-order logic with team semantics does not increase its expressive power
(with respect to sentences), and that the same remains true if we also add
constancy atoms. As a consequence, the negations of functional dependence,
conditional independence, inclusion and exclusion atoms can all be added to
first-order logic without increasing its expressive power.
Furthermore, we define a class of bounded upwards closed dependencies and we
prove that unbounded dependencies cannot be defined in terms of bounded ones.Comment: In Proceedings GandALF 2013, arXiv:1307.416
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