Bayesian network structures and inference techniques for automatic speech recognition

A Conditional Probability Model to Predict the Mortality in Patients With Breast Cancer: A Bayesian Network Analysis

Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

A Bayesian network (BN) Charniak (1991) Pearl (1988) Jensen (1996) Neapolitan (1990) is a directed acyclic graph (DAG) consisting of nodes and arrows, in which node represents random variables, and arrow represents dependence relationship between connected nodes in the sense of the probabilistic, deterministic, or functional. Each node in BN has a specified conditional probability distribution (CPD), where all CPDs together parameterize the model. BNs have been used as powerful probabilistic knowledge models for decision support under uncertainty over a few decades, with numerous applications such as classification, medical diagnosis, bioinformatics, speech recognition, etc. One of the most important features BN has is the factorization of the joint probability space, so that conditional independence can be exploited to simplify modeling and save computations. However, BN model is only useful when combined with efficient algorithms for inference.

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn reduces the search sample space. The method of combining Association Rules algorithm with traditional BN structure learning results is used to solve the problem of wrong edges in structure learning. Finally, the maximum likelihood estimation method is used for parameter learning to complete the construction of the diagnostic network. The experimental results show that the running time of the Bayesian Network based on the Association Rules (AR-BN) model proposed in this paper is short and that the Hamming distance with the original structure is small, so this model can effectively reduce the search space and solve the problem of wrong edges, and it also has a good performance in fault diagnosis.

This study aims to comprehensively identify risks to food safety and halal status in food manufacturing processes. The research was conducted through risk identification, data collection, Bayesian Network (BN) structure, decision analysis, and mitigation programs. The results showed that there were 19 integrated risks to food safety and halal. Twelve risks were due to food safety and seven risks were due to halal status. Overall, risks identified were described through the BN structure, which consists of four levels, as a basis for determining risk opportunity values. The results of BN decision analysis show that the highest integrated risk to food safety and halal each level. The risk of chickens dying when stunning is the highest risks in level 1 and cleaning and chilling department in level 2. The partially integrated risks for level 3 and total integrated risks in level 4 has a chance of contamination. Due is the results, the analysis and decision analysis is to reject or rework.

Automatic speech recognition (ASR) has gained wide popularity in last decade. Various devices like mobile phones, computers, vehicles, and audio/video players are now being equipped with ASR technology. The increasing use and dependence on ASR technology leads to research enhancements and opportunities in this domain. This chapter provides a detailed review of various advancements in ASR systems development. It highlights history of speech recognition followed by detailed insight into recent advancements and industry leaders providing latest solutions. ASR framework has been discussed in detail which includes feature extraction techniques, acoustic modeling techniques, and language modeling techniques. The chapter also lists various popular data sets available and discusses generation of new data sets. This work will be helpful for the researchers who are new to this field and are exploring development of new speech recognition techniques.

Inference in hybrid Bayesian networks with large discrete and continuous domains

Bayesian networks are powerful in data mining and analyzing causal relationships of an uncertain-reasoning problem. The implementation of Bayesian networks in industry and healthcare diagnosis can facilitate the process of locating causations in complex issues. This study conducted two case studies by BayesiaLab in consumer service and healthcare domain. Case Study One used unsupervised learning and supervised learning on the individual data set of county road traffic volume in Indiana State and concluded that road type has the most significant impact on daily vehicle miles traveled. In Case Study Two, only supervised learning was used to observe the aggregated data of adverse mental health effect on civilians, deployed veterans and nondeployed veterans of different genders. Both types of veterans showed higher probability to have adverse mental health compared to civilians. In conclusion, Bayesian networks provided valid results to support prior research. Further research is needed to investigate the differences between using individual data and aggregated data, and to apply Bayesian networks in meta-analysis.

A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Bayesian network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. Finding an optimal network structure based on an available training dataset is one of the most important challenges in the field of BNs. Since the problem of searching the optimal BN structure belongs to the class of NP-hard problems, typically greedy algorithms are used to solve it. In this paper two novel learning automata-based algorithms are proposed to solve the BNs’ structure learning problem. In both, there is a learning automaton corresponding with each possible edge to determine the appearance and the direction of that edge in the constructed network; therefore, we have a game of learning automata, at each stage of the proposed algorithms. Two special cases of the game of the learning automata have been discussed, namely, the game with a common payoff and the competitive game. In the former, all the automata in the game receive a unique payoff from the environment, but in the latter, each automaton receives its own payoff. As the algorithms proceed, the learning processes focus on the BN structures with higher scores. The use of learning automata has led to design the algorithms with a guided search scheme, which can avoid getting stuck in local maxima. Experimental results show that the proposed algorithms are capable of finding the optimal structure of BN in an acceptable execution time; and compared with other search-based methods, they outperform them.

Construction of Bayesian network structures from data: A brief survey and an efficient algorithm

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.

Efficient Bayesian network modeling of systems