74 research outputs found
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
Polarization Versus Agglomeration
The aim of this paper is to analyze the processes of polarization and
agglomeration, to explain the mechanisms and causes of these phenomena in order
to identify similarities and differences. As the main implication of this study
should be noted that both process pretend to explain the concentration of
economic activity and population in certain places, through cumulative
phenomena, but with different perspectives, in other words, the polarization
with a view of economic development and agglomeration with a perspective of
space
Polarization versus agglomeration
The aim of this paper is to analyze the processes of polarization and agglomeration, to explain the mechanisms and causes of these phenomena in order to identify similarities and differences. As the main implication of this study should be noted that both process pretend to explain the concentration of economic activity and population in certain places, through cumulative phenomena, but with different perspectives, in other words, the polarization with a view of economic development and agglomeration with a perspective of space.polarization; agglomeration; economic activity
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Several diseases related to cell proliferation are characterized by the
accumulation of somatic DNA changes, with respect to wildtype conditions.
Cancer and HIV are two common examples of such diseases, where the mutational
load in the cancerous/viral population increases over time. In these cases,
selective pressures are often observed along with competition, cooperation and
parasitism among distinct cellular clones. Recently, we presented a
mathematical framework to model these phenomena, based on a combination of
Bayesian inference and Suppes' theory of probabilistic causation, depicted in
graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are
generative probabilistic graphical models that recapitulate the potential
ordering of accumulation of such DNA changes during the progression of the
disease. Such models can be inferred from data by exploiting likelihood-based
model-selection strategies with regularization. In this paper we discuss the
theoretical foundations of our approach and we investigate in depth the
influence on the model-selection task of: (i) the poset based on Suppes' theory
and (ii) different regularization strategies. Furthermore, we provide an
example of application of our framework to HIV genetic data highlighting the
valuable insights provided by the inferred
TiO2 Breakdown Under Pulsed Conditions
Model studies of current conduction and breakdown in TiO2 were carried out. Our simulation results indicate that electrical breakdown of TiO2 under multiple-pulsed conditions can occur at lower voltages as compared to quasi-dc biasing. This is in agreement with recent experimental data and is indicative of a cumulative phenomena. We demonstrate that the lower breakdown voltages observed in TiO2 under pulsed conditions is a direct rise-time effect, coupled with successive detrapping at the grain boundaries. 2007 American Institute of Physics
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