نتایج جستجو برای: bayesian networks bns
تعداد نتایج: 498413 فیلتر نتایج به سال:
Due to its static nature, the inference capability of Bayesian Networks (BNs) often deteriorates when the basis of input data varies, especially in video processing applications where the environment often changes constantly. This paper presents an adaptive BN where the network parameters are adjusted in accordance to input variations. An efficient re-training method is introduced for updating ...
Boolean networks (BNs) are discrete-time dynamical systems with Boolean state-variables and outputs. BNs are recently attracting considerable interest as computational models for genetic and cellular networks. We consider the observability of BNs, that is, the possibility of uniquely determining the initial state given a time sequence of outputs. Our main result is that determining whether a BN...
One of the critical issues when adopting Bayesian networks (BNs) to model dependencies among random variables is to “learn” their structure, given the huge search space of possible solutions, i.e., all the possible direct acyclic graphs. This is a wellknown NP -hard problem, which is also complicated by known pitfalls such as the issue of I-equivalence among different structures. In this work w...
In this paper, we introduce a probabilistic framework based on the Bayesian Networks (BNs) for modelling and real-time inferring human fatigue by integrating information from various visual cues and certain relevant contextual information. We first present a static Bayesian network that captures the static relationships between fatigue, significant factors that cause fatigue, and various visual...
MOTIVATION For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional da...
Bayesian Networks (BNs) model problems that involve uncertainty. A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or influential links between the variables. Associated with each node is a set of conditional probability functions that model the uncertain relationship between the node and its parents. The benefits of using BNs to model uncertain do...
and Carlo Giupponi Ca’ Foscari University, Fondazione Eni Enrico Mattei and Centro Euro-Mediterraneo per i Cambiamenti Climatici SUMMARY Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effe...
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its first contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) lear...
Bayesian networks (BNs) are an efficient way of representing joint probability distributions over sets of random variables; they are commonly employed in AI for reasoning under uncertainty.1 A BN is made up of two components: a directed acyclic graph (DAG), whose nodes represent random variables, and a set of conditional probability tables (CPTs), specifying the conditional probability distribu...
Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes...
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