More Complex Cases: General Bayesian Networks

Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditional interval probabilities and the probabilistic description, as well as the multiplication rules. Furthermore, we propose the method for learning the Bayesian network structure from interval data and the algorithm for corresponding approximate inferences. Experimental results show that our methods are feasible, and we conclude that the Bayesian network with interval probability parameters is the expansion of the general Bayesian network.

General Bayesian Network Approach to Health Informatics Prediction: Emphasis on Performance Comparison

This paper proposes the general Bayesian network as a tool for identifying critical dependencies among complex human beliefs, especially for the case of airline service industry. When we consider an overall efficacy of the flight operation, to understand underlying beliefs between pilots and controllers is critical. However, even for the experts in organizational behavior, it is difficult to identify dependencies among such the beliefs. To tackle this ambiguous issue, we suggest the use of the general Bayesian network to automatically generate interesting hypotheses. The experimental results reveal the possibility of selective attention to the critical dependencies for achieving enhanced efficacy.

2U: an exact interval propagation algorithm for polytrees with binary variables

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a machine) for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a Markov backbone.

The football match outcome prediction particularly has gained popularity in recent years. It attract lots type of fan from the analyst expert, managerial of football team and others to predict the football match result before the match start.There are three types of approaches had been proposed to predict win, lose or draw; and evaluate the attributes of the football team. The approaches are statistical approach, machine learningapproach and Bayesian approach. This paper propose the Bayesian approaches within machine learning approaches such as Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Network (K2) to predict the football match outcome. The required of football data is the English Premier League match results for three seasons; 2016 – 2017, 2015 – 2016 and 2014 – 2015 downloaded from http://www.football-data.co.uk. The experimental results showed that TAN achieved the highest predictive accuracy of 90.0 % in average across three seasons among others Bayesian approach (K2 and NB). The result from this research is hope that it can be used in future research for predicting the football match outcome.

This paper deals with the problem of coordinating a team of mobile sensor platforms searching for a single mobile non-evading target. It follows the general Bayesian active sensor network approach introduced in [2] where each decision maker plans locally based on an equivalent representation of the target state probability density function (PDF). This paper focuses on the prediction stage of the decentralized Bayesian filter. It looks at how different types of realistic external constraints may affect the target motion and how they may be taken into account in the process model. Two general classes of constraints are identified soft and hard. A few constraint examples from each class are given to illustrate their impact on the evolution of the target state PDF. Multiple constraints of various types can be combined to increase the accuracy of the predicted PDF estimate, thus affecting the individual trajectories of the search platforms. The effectiveness of the framework is demonstrated for a team of airborne search vehicles looking for a drifting target lost in a storm at sea.

In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation. Moreover, we develop a search algorithm for optimal feature selection that utilizes efficient incremental circuit modifications. Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach.

Article Free Access Share on The calculation of average distance in mesh structures Author: Zhizhang Shen Department of Computer Science, Plymouth State College (USNH), Plymouth, NH Department of Computer Science, Plymouth State College (USNH), Plymouth, NHView Profile Authors Info & Claims SAC '00: Proceedings of the 2000 ACM symposium on Applied computing - Volume 1March 2000Pages 89–93https://doi.org/10.1145/335603.335707Published:19 March 2000Publication History 3citation475DownloadsMetricsTotal Citations3Total Downloads475Last 12 Months51Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Bayesian network structures and inference techniques for automatic speech recognition

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

In this paper we discuss some topics of knowledge representation for reasoning under uncertainty in belief network-based systems. In particular, we will examine the seemly obvious but unduly overlooked phenomenon that belief functions can grow at an exponential rate when using Dempster's rule of combination, which often results in belief computations being done in nearly worst cases. This problem has severe practical consequences for the development of belief network-based systems in applications domains where the knowledge structure determines a dense belief network with high degrees of node linkage and especially with large clusterings of belief functions. Empirical evidence suggests that belief networks for some types of problem domain like classification tend to be very dense. Despite the development of efficient local computation schemes for belief propagation in general Dempster-Shafer belief networks, it can be concluded that the actual applicability of belief networks is limited subject to the knowledge structure of the problem domain under consideration.

Planning sustainable supply chains (SCs) for emerging biocomposite materials is critical for balancing environmental, economic, and social dimensions in strategic decision-making, towards industrial success. In biocomposite SCs, early-stage pre-processing activities, such as biomass collection and particle size reduction, play a pivotal role in determining downstream sustainability outcomes. However, evaluating the performance of different SC scenarios under changing conditions remains difficult due to the non-linear interdependencies and uncertainty across SC components. This study presents a knowledge-driven Bayesian Belief Network (BBN) framework to support sustainability assessment of a complex hemp-based biocomposite SC case study under uncertainty. A novel metric, the Supply Chain Sustainability Index (SCSI), is introduced to quantify the overall probability of achieving the target sustainability performance level and assess the vulnerability of each underlying indicator. The BNN model integrates both expert insights and empirical relationships through regression-informed Conditional Probability Tables (CPTs) and causal graphs, across 15 proposed criteria of measurement spanning economic (e.g., Net Present Value (NPV), Conditional Value at Risk (CVaR), costs), technical (e.g., product quality, Technology Readiness Level (TRL)), social (e.g., job creation), and environmental (e.g., carcinogenic and ecotoxic impacts) factors. The scenario-based simulation and entropy-based sensitivity analyses are also conducted to identify the most influential factors among the criteria trade-offs. The results showed that the economic and technical factors, in the present case study, have the greatest influence on overall sustainability, while the social and environmental indicators revealed comparatively moderate effects.

Increasingly complex economic backgrounds and power consumption have created more difficulties for medium and long-term load forecasting. Traditional load forecasting models such as linear regression and time series models have a good effect on linear data, but the accuracy of nonlinear electric load data in Xinjiang is poor. The BP neural network model has better processing ability for nonlinear data, but it also has the problem of poor fitting and generalization ability. Aiming at this problem, this paper establishes a medium- and long-term power load forecasting model of Bayesian regularized BP neural network. Compared with the general gradient descent method BP neural network model, it is proved that the proposed model has a good effect on the electricity consumption forecast in Xinjiang.

Single channel source separation (SCSS) is ill-posed and thus challenging. In this paper, we apply general stochastic networks (GSNs) – a deep neural network architecture – to SCSS. We extend GSNs to be capable of predicting a time-frequency representation, i.e. softmask by introducing a hybrid generative-discriminative training objective to the network. We evaluate GSNs on data of the 2nd CHiME speech separation challenge. In particular, we provide results for a speaker dependent, a speaker independent, a matched noise condition and an unmatched noise condition task. Empirically, we compare to other deep architectures, namely a deep belief network (DBN) and a multi-layer perceptron (MLP). In general, deep architectures perform well on SCSS tasks.