scholarly journals Handling Overlaps When Lifting Gaussian Bayesian Networks

2021 ◽  
Author(s):  
Mattis Hartwig ◽  
Tanya Braun ◽  
Ralf Möller
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Real World ◽  
Joint Distribution ◽  
Random Variables ◽  
Query Answering ◽  
Experimental Results ◽  
Compact Representation ◽  
Large Numbers ◽  
Gaussian Bayesian Network

Gaussian Bayesian networks are widely used for modeling the behavior of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables. It provides a more compact representation for more efficient query answering by encoding the symmetries using logical variables. This paper improves on an existing lifted representation of the joint distribution represented by a Gaussian Bayesian network (lifted joint), allowing overlaps between the logical variables. Handling overlaps without grounding a model is critical for modelling real-world scenarios. Specifically, this paper contributes (i) a lifted joint that allows overlaps in logical variables and (ii) a lifted query answering algorithm using the lifted joint. Complexity analyses and experimental results show that - despite overlaps - constructing a lifted joint and answering queries on the lifted joint outperform their grounded counterparts significantly.

scholarly journals Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Linda Smail
Keyword(s):  
Artificial Intelligence ◽  
Probability Distribution ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Probabilistic Models ◽  
Probability Distributions ◽  
Random Variables ◽  
Inference Problem ◽  
Computation Algorithm

Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade. This paper presents Bayesian networks and discusses the inference problem in such models. It proposes a statement of the problem and the proposed method to compute probability distributions. It also uses D-separation for simplifying the computation of probabilities in Bayesian networks. Given a Bayesian network over a family of random variables, this paper presents a result on the computation of the probability distribution of a subset of using separately a computation algorithm and D-separation properties. It also shows the uniqueness of the obtained result.

Application of Bayesian Networks for Diagnosis Analysis of Modified Sequencing Batch Reactor

2012 ◽  
Vol 610-613 ◽  
pp. 1139-1145 ◽  
Author(s):  
Dan Li ◽  
Hai Zhen Yang ◽  
Xiao Feng Liang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Reaction Mechanisms ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Experimental Results ◽  
Diagnostic Analysis ◽  
Analysis Algorithm ◽  
Biological Reaction ◽  
One Year

The diagnosis analysis of MSBR has remained difficult due to the complexity of biological reaction mechanisms and the involvement of highly non-linearity and uncertainty. In this paper, the Bayesian network was used to modeling and diagnosis analysis of a MSBR. The suggested BN model for MSBR was evaluated using one-year of operation data. Results showed that the BN-based model for MSBR is reasonable and the prediction analysis algorithm is feasible. According to the framework of the diagnostic analysis, an example was given to illustrate the detailed information of diagnostic of a MSBR. Experimental results indicated that the corrective measures based on the diagnostic analysis were reasonable.

Software Project and Quality Modelling Using Bayesian Networks

2011 ◽  
pp. 1-25 ◽  
Author(s):  
Norman Fenton ◽  
Peter Hearty ◽  
Martin Neil ◽  
Lukasz Radlinski
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Real World ◽  
Causal Reasoning ◽  
Software Project ◽  
Limited Data ◽  
Short Overview ◽  
Software Development Environments

This chapter provides an introduction to the use of Bayesian Network (BN) models in Software Engineering. A short overview of the theory of BNs is included, together with an explanation of why BNs are ideally suited to dealing with the characteristics and shortcomings of typical software development environments. This theory is supplemented and illustrated using real world models that illustrate the advantages of BNs in dealing with uncertainty, causal reasoning and learning in the presence of limited data.

scholarly journals Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhipeng Fang ◽  
Kun Yue ◽  
Jixian Zhang ◽  
Dehai Zhang ◽  
Weiyi Liu
Keyword(s):  
Bayesian Network ◽  
Real World ◽  
Search Engines ◽  
Historical Data ◽  
Experimental Results ◽  
Real World Applications

Most classical search engines choose and rank advertisements (ads) based on their click-through rates (CTRs). To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN) as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method.

BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS

2005 ◽  
Vol 03 (01) ◽  
pp. 61-77 ◽  
Author(s):  
JEONG-HO CHANG ◽  
KYU-BAEK HWANG ◽  
S. JUNE OH ◽  
BYOUNG-TAK ZHANG
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Human Cancer ◽  
Expression Profiles ◽  
Compact Representation ◽  
Joint Probability Distribution ◽  
Gene Expressions ◽  
Drug Activity ◽  
Human Cancer Cell Lines ◽  
Original Dataset

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset () consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

scholarly journals Vehicular Mobility Prediction by Bayesian Networks

2007 ◽  
Vol 36 (582) ◽  
Author(s):  
Kari Schougaard
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Ubiquitous Computing ◽  
Common Sense ◽  
Real World ◽  
Mobility Prediction ◽  
Good Prediction ◽  
Inaccurate Information ◽  
Common Sense Knowledge ◽  
Constrained Devices

In mobile and ubiquitous computing the location of devices<br />is often important both for the behavior of the applications<br />and for communication and other middleware functionality.<br />Mobility prediction enables proactively dealing<br />with changes in location dependent functionality. In this<br />project Bayesian networks’ ability to reason on the basis of<br />incomplete or inaccurate information is powering mobility<br />prediction based on a map of the street grid and the current<br />location and direction of the vehicle. We found that it<br />is feasible to divide information of a map into smaller parts<br />and generate a Bayesian network for each of these in order<br />to make mobility prediction based on localized information.<br />This makes the information stored in the Bayesian networks<br />more manageable in size, which is important for resource<br />constrained devices. Common sense knowledge of how vehicle<br />moves is feeded into the networks and enables them<br />to make a good prediction even when no information of the<br />vehicles mobility history is used. Experiments on real world<br />data show that in an area statically divided into hexagonal<br />cells of 200m in diameter, we get 80.54% accuracy when<br />using localized Bayesian networks to predict which cell a<br />vehicle enters next.

DIRECTING GENETIC ALGORITHMS FOR PROBABILISTIC REASONING THROUGH REINFORCEMENT LEARNING

2000 ◽  
Vol 08 (02) ◽  
pp. 167-185 ◽  
Author(s):  
XIAOMIN ZHONG ◽  
EUGENE SANTOS
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Reinforcement Learning ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Belief Revision ◽  
Probabilistic Reasoning ◽  
Random Variables

In this paper, we develop an efficient online approach for belief revision over Bayesian networks by using a reinforcement learning controller to direct a genetic algorithm. The random variables of a Bayesian network can be grouped into several sets reflecting the strong probabilistic correlations between random variables in the group. We build a reinforcement learning controller to identify these groups and recommend the use of "group" mutation and "group" crossover for the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm and continues with reinforcement learning to further tune the controller to search for a better grouping.

scholarly journals Learning Continuous Time Bayesian Networks in Non-stationary Domains

2018 ◽  
Author(s):  
Simone Villa ◽  
Fabio Stella
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Real World ◽  
Continuous Time ◽  
Real World Data ◽  
Change Over Time ◽  
World Data ◽  
Continuous Time Bayesian Networks ◽  
Continuous Time Bayesian Network ◽  
Over Time

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Combined Games with Randomly Delayed Beginnings

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 534
Author(s):  
F. Thomas Bruss
Keyword(s):  
Discrete Time ◽  
Real World ◽  
Optimal Stopping ◽  
Random Variables ◽  
Approximate Solutions ◽  
Real World Problems

This paper presents two-person games involving optimal stopping. As far as we are aware, the type of problems we study are new. We confine our interest to such games in discrete time. Two players are to chose, with randomised choice-priority, between two games G1 and G2. Each game consists of two parts with well-defined targets. Each part consists of a sequence of random variables which determines when the decisive part of the game will begin. In each game, the horizon is bounded, and if the two parts are not finished within the horizon, the game is lost by definition. Otherwise the decisive part begins, on which each player is entitled to apply their or her strategy to reach the second target. If only one player achieves the two targets, this player is the winner. If both win or both lose, the outcome is seen as “deuce”. We motivate the interest of such problems in the context of real-world problems. A few representative problems are solved in detail. The main objective of this article is to serve as a preliminary manual to guide through possible approaches and to discuss under which circumstances we can obtain solutions, or approximate solutions.

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