Probabilistic Reasoning in Bayesian Networks: A Relational Database Approach

2003 ◽  
pp. 583-590 ◽  
Author(s):  
S. K. Michael Wong ◽  
Dan Wu ◽  
Cory J. Butz
Keyword(s):  
Bayesian Networks ◽  
Relational Database ◽  
Probabilistic Reasoning

scholarly journals Comparing the electrophysiology and morphology of human and mouse layer 2/3 pyramidal neurons with Bayesian networks

2020 ◽  
Author(s):  
Bojan Mihaljević ◽  
Pedro Larrañaga ◽  
Concha Bielza
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Reasoning ◽  
Pyramidal Neurons ◽  
Temporal Cortex ◽  
Input Resistance ◽  
Cell Types ◽  
Dendritic Arbor ◽  
Basal Dendrites ◽  
Morphological Variables ◽  
Human And Mouse

ABSTRACTPyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and basal dendrites’ morphology. We found that, among other differences, human pyramidal neurons had a higher threshold voltage, a lower input resistance, and a larger basal dendritic arbor. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. One result is that, in human cells, dendritic arbor width had the strongest effect on input resistance after accounting for the remaining variables. Electrophysiological variables were correlated, in both species, even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Contrary to previous results, cortical depth was correlated with both electrophysiological and morphological variables, and its effect on electrophysiological could not be explained in terms of the morphological variables. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.

Bayesian Agencies in Control

2011 ◽  
pp. 168-181
Author(s):  
Anet Potgieter ◽  
Judith Bishop
Keyword(s):  
Bayesian Networks ◽  
Autonomous Agents ◽  
Probabilistic Reasoning ◽  
Multi Agent Systems ◽  
Agent Architectures ◽  
Analysis And Design ◽  
Agent Systems ◽  
Interaction Protocols ◽  
Multi Agent ◽  
The Individual

Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications

2004 ◽  
Vol 8 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Yoichi Motomura ◽  
Keyword(s):  
Information Processing ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Statistical Learning ◽  
Probabilistic Models ◽  
Probabilistic Reasoning ◽  
Network Models ◽  
Intelligent Information Processing ◽  
Intelligent Information ◽  
Bayesian Network Models

Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.

GLOBAL PROPAGATION IN BAYESIAN NETWORKS VS SEMIJOIN PROGRAMS IN RELATIONAL DATABASES

2005 ◽  
Vol 13 (05) ◽  
pp. 539-560
Author(s):  
DAN WU ◽  
MICHAEL WONG
Keyword(s):  
Bayesian Networks ◽  
Relational Databases ◽  
Probabilistic Reasoning ◽  
Uncertainty Management ◽  
Query Answering ◽  
Knowledge Representations

Bayesian networks have been well established as an effective framework for uncertainty management using probability. Various methods for probabilistic reasoning in Bayesian networks have been developed and matured. Recently, research has shown that there exists an intriguing relationship between Bayesian networks and relational databases. Adding to that intriguing relationship, in this paper, we reveal that the global propagation method for probabilistic reasoning in Bayesian networks has a close tie with the well known semijoin programs for query answering in relational databases. This linkage between these two apparently different but closely related knowledge representations suggests that well developed techniques for query answering in relational databases could be applied to probabilistic reasoning in Bayesian networks for large and complex domains.

scholarly journals Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Bojan Mihaljević ◽  
Pedro Larrañaga ◽  
Concha Bielza
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Reasoning ◽  
Pyramidal Neurons ◽  
Temporal Cortex ◽  
Input Resistance ◽  
Cell Types ◽  
Dendritic Morphology ◽  
Morphological Variables ◽  
Mouse Visual Cortex ◽  
Human And Mouse

Pyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and dendritic morphology. We found that, among other differences, human pyramidal neurons had a higher action potential threshold voltage, a lower input resistance, and larger dendritic arbors. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. In human cells, electrophysiological variables were correlated even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Cortical depth was correlated with both electrophysiological and morphological variables in both species, and its effect on electrophysiology could not be explained in terms of the morphological variables. For some variables, the effect of cortical depth was opposite in the two species. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.

scholarly journals Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
LiMin Wang
Keyword(s):  
Bayesian Network ◽  
Relational Database ◽  
Network Inference ◽  
Probabilistic Reasoning ◽  
Expert Knowledge ◽  
Learning Framework ◽  
Three Phase ◽  
Rule Analysis ◽  
Effectiveness And Efficiency ◽  
Bayesian Network Inference

The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved. On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN) with the functional dependency (FD) discovery technique. Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized. Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed. Experimental results show the effectiveness and efficiency of the proposed approach.

scholarly journals BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks

2020 ◽  
Vol 94 (3) ◽  
Author(s):  
Han Yu ◽  
Janhavi Moharil ◽  
Rachael Hageman Blair
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Reasoning ◽  
R Package
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