Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks

Lucas, Peter; Hommersom, Arjen

doi:10.1002/int.21698

Cited by 6 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A probabilistic system must be analyzed according to the various possible outcomes and their relative probability of occurrence. In the literature two types of probabilistic systems [103] are discussed using HMM models i.e. Discrete Probability system (DPS), continuous Probability system (CPS) and Bayesian Probability system(BPS) a) Bayesian Probability: It gives a mathematical framework for performing inference using probability.…”

Section: Probabilistic Systemsmentioning

confidence: 99%

Fuzzy Speech Recognition: A Review

Vani¹,

Anusuya²

2020

IJCA

View full text Add to dashboard Cite

The area of speech recognition is one of the interesting field in speech signal processing. Achieving accuracy and robustness is a very difficult constraint to various environmental factors. Progressive work and reviews in the speech recognition application has been adopted using fuzzy, as one of the technique to improve the recognition accuracies. This review paper reviews the various concepts of fuzzy technique and its applications to speech signal processing area. Since the nature of speech signal is vague, it does not pocess uniformity at all time intervals. To deal with this vagueness and uncertainties, many researchers have suggested fuzzy is one of the better technique to analyze the speech signals. This paper presents the literature work available related to speech recognition using fuzzy techniques.

show abstract

Section: Probabilistic Systemsmentioning

confidence: 99%

Fuzzy Speech Recognition: A Review

Vani¹,

Anusuya²

2020

IJCA

View full text Add to dashboard Cite

show abstract

“…Because of their suitability to offer a more comprehensive prediction of the conditional probability of a specific event, and also having the capability for developing a hierarchy of prognostic triage levels based on the expected outcomes, probability-based bioinformatics tools, rooted in the Bayesian principle [37][38], can offer a useful, dynamic and adaptable platform for developing such improved guidelines.…”

Section: Comment On This Article or Ask A Questionmentioning

confidence: 99%

Dissecting the Dissection

Sherif

2015

Aorta

View full text Add to dashboard Cite

Aortic dissection remains one of the most devastating diseases. Current practice guidelines provide diagnostic and therapeutic interventions based primarily on the aortic diameter. The level of evidence supporting these recommendations is Level C or "Expert Opinion" Since aortic dissection is a catastrophic structural failure, its investigation along the guidelines of accident investigation may offer a useful alternative, utilizing process mapping and root-cause analysis methodology. Since the objective of practice guidelines is to address the risk of serious events, on the utilization of a probabilistic predictive modeling methodology, using bioinformatics tools, may offer a more comprehensive risk assessment.

show abstract

“…Cobb and Li 15,16 further developed a Bayesian Network (BN) to monitor a production process where categorical attribute data are available. Lucas and coworkers 17,18 developed and generalized the theory of causal independence to allow a merging of continuous and discrete parameters. Thanks to its rigorous mathematical foundation and flexible implementation, the Bayesian framework incorporates both subjective judgment and objective test data into a comprehensive statistical model.…”

Section: Introductionmentioning

confidence: 99%

On investigation of the Bayesian anomaly in multiple imprecise dependent information aggregation for system reliability evaluation

et al. 2021

View full text Add to dashboard Cite

The increasing complexity of the modern engineering system has made the multisource information fusion a necessary yet challenging task. In the context of reliability engineering, the information fusion process is either ineffective or less efficient as the aggregation error increases with respect to the collection of multiple dependent pieces of evidence. To address this challenge, this paper proposes a comprehensive Bayesian approach for system reliability evaluation that considers multiple, dependent sources of information. We show that the so‐called “Bayesian anomaly,” a type of aggregation error, is caused by misuse of the dependent information and could be eliminated if all available information is properly addressed. A topological technique is employed as a tool for information fusion in the likelihood construction. A likelihood‐based approach is then developed to formulate the overall likelihood as well as the reliability model. These two techniques are embedded into a comprehensive Bayesian framework to allow for an efficient evaluation of the system reliability. Our approach has also been extended to include imprecise information, such as interval and/or censored data, which is more frequently encountered in practical engineering. We demonstrate the proposed method through several numerical cases and one real‐life application example. This study provides a better understanding of the role of dependent information in system reliability evaluation. In addition, it presents an efficient pathway to extract the inherent dependency information embedded in imperfect data sets.

show abstract

Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks

Cited by 6 publications

References 38 publications

Fuzzy Speech Recognition: A Review

Fuzzy Speech Recognition: A Review

Dissecting the Dissection

On investigation of the Bayesian anomaly in multiple imprecise dependent information aggregation for system reliability evaluation

Contact Info

Product

Resources

About