174 research outputs found
Learning Markov networks with context-specific independences
Learning the Markov network structure from data is a problem that has
received considerable attention in machine learning, and in many other
application fields. This work focuses on a particular approach for this purpose
called independence-based learning. Such approach guarantees the learning of
the correct structure efficiently, whenever data is sufficient for representing
the underlying distribution. However, an important issue of such approach is
that the learned structures are encoded in an undirected graph. The problem
with graphs is that they cannot encode some types of independence relations,
such as the context-specific independences. They are a particular case of
conditional independences that is true only for a certain assignment of its
conditioning set, in contrast to conditional independences that must hold for
all its assignments. In this work we present CSPC, an independence-based
algorithm for learning structures that encode context-specific independences,
and encoding them in a log-linear model, instead of a graph. The central idea
of CSPC is combining the theoretical guarantees provided by the
independence-based approach with the benefits of representing complex
structures by using features in a log-linear model. We present experiments in a
synthetic case, showing that CSPC is more accurate than the state-of-the-art IB
algorithms when the underlying distribution contains CSIs.Comment: 8 pages, 6 figure
Exploiting Causal Independence in Bayesian Network Inference
A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further factorize the conditional probabilities into a
combination of even smaller factors and consequently obtain a finer-grain
factorization of the joint probability. The new formulation of causal
independence lets us specify the conditional probability of a variable given
its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a
simple algorithm VE for Bayesian network inference that, given evidence and a
query variable, uses the factorization to find the posterior distribution of
the query. We show how this algorithm can be extended to exploit causal
independence. Empirical studies, based on the CPCS networks for medical
diagnosis, show that this method is more efficient than previous methods and
allows for inference in larger networks than previous algorithms.Comment: See http://www.jair.org/ for any accompanying file
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Despite their immense success in numerous fields, machine and deep learning
systems have not yet been able to firmly establish themselves in
mission-critical applications in healthcare. One of the main reasons lies in
the fact that when models are presented with previously unseen,
Out-of-Distribution samples, their performance deteriorates significantly. This
is known as the Domain Generalization (DG) problem. Our objective in this work
is to propose a benchmark for evaluating DG algorithms, in addition to
introducing a novel architecture for tackling DG in biosignal classification.
In this paper, we describe the Domain Generalization problem for biosignals,
focusing on electrocardiograms (ECG) and electroencephalograms (EEG) and
propose and implement an open-source biosignal DG evaluation benchmark.
Furthermore, we adapt state-of-the-art DG algorithms from computer vision to
the problem of 1D biosignal classification and evaluate their effectiveness.
Finally, we also introduce a novel neural network architecture that leverages
multi-layer representations for improved model generalizability. By
implementing the above DG setup we are able to experimentally demonstrate the
presence of the DG problem in ECG and EEG datasets. In addition, our proposed
model demonstrates improved effectiveness compared to the baseline algorithms,
exceeding the state-of-the-art in both datasets. Recognizing the significance
of the distribution shift present in biosignal datasets, the presented
benchmark aims at urging further research into the field of biomedical DG by
simplifying the evaluation process of proposed algorithms. To our knowledge,
this is the first attempt at developing an open-source framework for evaluating
ECG and EEG DG algorithms.Comment: Accepted in IEEE Transactions on Emerging Topics in Computational
Intelligenc
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Survey of Bayesian Models for Modelling of Stochastic Temporal Processes
This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks
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The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications
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