Implementation Of Bayesian Networks Research Articles

Bayesian networks supporting management practices: A multifaceted perspective based on the literature

Bayesian network is a probabilistic graphical model within machine learning that supports decision-making under conditions of uncertainty in different domains. Although the scientific literature has increasingly addressed the implementation of Bayesian networks to support management practices (BNM), a thorough review is currently lacking. This bibliometric review investigates the transformative potential of Bayesian networks in reshaping decision-making paradigms across multidisciplinary domains. The knowledge set findings reveal a predominant focus on risk management within the Engineering domain; the scientific openings involve significant progress in both theoretical frameworks and practical applications across Computer Science, Engineering, Medicine, and Environmental Science; and research trends indicate a progressive BNM within Engineering and Medicine, contrasting with a decline in innovative studies related to Computer Science. This study acts as a catalyst, propelling inventive BNM applications and fostering interdisciplinary advancements. It lays a foundation for pioneering BNM strategies.

Implementation of Bayesian Network Based on Ultra-High-Speed Superconductor Random Number Generators

Implementation of Bayesian Network Based on Ultra-High-Speed Superconductor Random Number Generators

Hardware implementation of Bayesian network based on two-dimensional memtransistors

Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining ‘1’ in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications.

Towards an Assembly Support System with Dynamic Bayesian Network

Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.

Brain-Inspired Hardware Solutions for Inference in Bayesian Networks.

The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating-point operations. A departure from conventional computing systems to make use of the high parallelism of Bayesian inference has attracted recent attention, particularly in the hardware implementation of Bayesian networks. These efforts lead to several implementations ranging from digital circuits, mixed-signal circuits, to analog circuits by leveraging new emerging nonvolatile devices. Several stochastic computing architectures using Bayesian stochastic variables have been proposed, from FPGA-like architectures to brain-inspired architectures such as crossbar arrays. This comprehensive review paper discusses different hardware implementations of Bayesian networks considering different devices, circuits, and architectures, as well as a more futuristic overview to solve existing hardware implementation problems.

Hardware implementation of Bayesian network building blocks with stochastic spintronic devices

Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the associated Bayesian networks become computationally intractable. As a result, direct hardware implementation of these networks is one promising approach to reducing power consumption and execution time. However, the few hardware implementations of Bayesian networks presented in literature rely on deterministic CMOS devices that are not efficient in representing the stochastic variables in a Bayesian network that encode the probability of occurrence of the associated event. This work presents an experimental demonstration of a Bayesian network building block implemented with inherently stochastic spintronic devices based on the natural physics of nanomagnets. These devices are based on nanomagnets with perpendicular magnetic anisotropy, initialized to their hard axes by the spin orbit torque from a heavy metal under-layer utilizing the giant spin Hall effect, enabling stochastic behavior. We construct an electrically interconnected network of two stochastic devices and manipulate the correlations between their states by changing connection weights and biases. By mapping given conditional probability tables to the circuit hardware, we demonstrate that any two node Bayesian networks can be implemented by our stochastic network. We then present the stochastic simulation of an example case of a four node Bayesian network using our proposed device, with parameters taken from the experiment. We view this work as a first step towards the large scale hardware implementation of Bayesian networks.

Multi‐Criteria Decision Analysis for Recreational Trout Fisheries in British Columbia, Canada: A Bayesian Network Implementation

AbstractOne of the key challenges in recreational fisheries management is to provide a rational basis for decisions in the face of conflicting objectives, such as improving angling opportunities, maintaining satisfaction across a diverse fishing client base, controlling costs, and conserving wild populations. We developed a multi‐criteria decision analysis approach for managing recreational trout fisheries (e.g., Rainbow Trout Oncorhynchus mykiss). The approach was implemented in a Bayesian decision network. The decision support tool, called “Stock‐Optim,” provides a user‐friendly interface for predicting fishery performance from alternate stocking prescriptions. The tool integrates survey information on angler typology and satisfaction with previously developed models for fish biology and fishery dynamics to more fully consider the biological and social outcomes of management decisions. Specifically, the tool evaluated alternative stocking options given three performance criteria: angler effort, angler satisfaction, and the cost of the stocking program. Predicted effort was highest for fish that were released in the size range of 8–20 g and at stocking densities of 200–500 fish/ha. Effort maximization at these rates and sizes is a result of compromise between the conflicting preferences of Rainbow Trout enthusiasts and occasional anglers toward fish size and harvest. Furthermore, lowering the stocking program's costs will lead to lower stocking rates and thereby favor the enthusiasts. Currently, stocking levels in British Columbia are lower than levels that would maximize effort and are most consistent with a policy of maximizing satisfaction for Rainbow Trout enthusiasts and minimizing costs. Stock‐Optim will allow managers to compare predicted outcomes from the current and alternative regimes with stated lake‐specific or region‐specific management objectives and regional averages and thereby more closely meet these objectives in the future. Lastly, the model was validated by comparing predicted effort with observed effort in stocked lakes.Received February 1, 2016; accepted July 14, 2016 Published online November 28, 2016

The Application of Data Mining Technology Based on Bayesian Network Structure in Food Science Learning

The paper investigates the implementation of Bayesian network in food science learning. Taking a brief introduction of data mining for the point cut of the study and combining an explanation for the data mining process and an analysis of Bayesian Network. Originated from Bayesian statistics, Bayesian network, with such characteristics as its unique expression form of uncertainty knowledge, rich probabilistic expression abilities and the incremental learning method for comprehensive prior knowledge, indicates the probability distributions and causal relations of objects, becoming one of the most striking focus among numerous current data mining methods.

Data Mining Technology Based on Bayesian Network Structure Applied in Learning

Originated from Bayesian statistics, Bayesian network,with such characteristics as its unique expression form of uncertainty knowledge, rich probabilistic expression abilities, and the incremental learning method for comprehensive priori knowledge,indicates the probability distributions and causal relations of objects, becoming one of the most striking focus among numerous current data mining methods. Taking a brief introduction of data mining for the point-cut of the study, and combining an explanation for the data mining process and an analysis of Bayesian Network, the paper investigates the implementation of Bayesian network applied in in learning.

Visual fatigue measurement model in stereoscopy based on Bayesian network

A stereoscopic visual fatigue measurement model based on Bayesian networks (BNs) is presented. Our approach focuses on the interdependencies between factors, such as contextual and environmental, and the phenomena of visual fatigue in stereoscopy. Specifically, the implementation of BN with the use of multiple features provides a systematic way to project and evaluate visual fatigue. Compared with another measurement model, our present BN-based scheme is more comprehensive. The test validation also indicates that our proposed model can be used as a reliable method for the visual fatigue inferring in stereoscopy.

State-and-transition modelling for Adaptive Management of native woodlands

Adaptive Management (AM) is widely advocated as an approach to dealing with uncertainty in natural resource management as it provides an explicit framework for motivating, designing and interpreting the results of monitoring. One of the major factors impeding implementation is the failure to use appropriate process models; a core element of AM. Process models represent beliefs about the properties and dynamics of an ecological system and ecosystem responses to management. Quantitative models of ecosystem response help resolve ambiguity about the efficacy of management and facilitate iterative updating of knowledge using monitoring data. We report on the use of a state-and-transition model (STM) in the Adaptive Management of native woodland vegetation in south-eastern Australia. The STM is implemented as a Bayesian network, making it simple to communicate and update with new data as they arise. Application of the model is demonstrated using case-study and simulation data. We show how the model may be used to predict the probability of achieving desirable state transitions at restoration sites and how monitoring of those sites can be used to update the model (learn) and adapt (review restoration strategies). After just one monitoring/learning cycle, 7 years after the first investments, we found that updated models predict markedly different transition probabilities compared with initial models based on expert opinion. This has strong implications for the apparent cost-efficiency of restoration strategies. The STM approach provides a sound theoretical basis for restoration decisions, while the Bayesian network implementation provides a workable framework for using the STM adaptively.

Probabilistic evidential assessment of gunshot residue particle evidence (Part II): Bayesian parameter estimation for experimental count data

Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models – in the form of Bayesian networks – address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.

Potentials of Bayesian Networks to Deal with Uncertainty in Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.

Bayesian network structures and inference techniques for automatic speech recognition

This paper describes the theory and implementation of Bayesian networks in the context of automatic speech recognition. Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speech recognition training and decoding. This approach is notable because it expresses all the details of a speech recognition system in a uniform way using only the concepts of random variables and conditional probabilities. A powerful set of computational routines complements the representational utility of Bayesian networks, and the second part of this paper describes these algorithms in detail. We present a novel view of inference in general networks – where inference is done via a change-of-variables that renders the network tree-structured and amenable to a very simple form of inference. We present the technique in terms of straightforward dynamic programming recursions analogous to HMM α– β computation, and then extend it to handle deterministic constraints amongst variables in an extremely efficient manner. The paper concludes with a sequence of experimental results that show the range of effects that can be modeled, and that significant reductions in error-rate can be expected from intelligently factored state representations.

Bayesian network implementation of Levi's epistemic utility decision theory

Isaac Levi has proposed an epistemic decision rule that requires two convex sets of probability distributions: a set of credal probability distributions that represent a decision agent's state of knowledge, and a set of information-determining distributions that represent the decision agent's assessment of the informational value of various hypotheses. In this paper, we investigate the feasibility of using Bayesian network structures, in which conditional probability distributions are computed using local computations and conditional independence relationships, to implement Levi's decision rule. We find that Bayesian network update algorithms do not in general result in convex sets of distributions; however, Bayesian networks can compute sets of a posteriori extremal distributions from sets of a priori and conditional extremal distributions. We also show that Levi's decision rule gives the same answer when applied to arbitrary sets of credal and information-determining distributions as it gives when applied to the convex closure of those sets of distributions. Thus, implementation of Levi's decision rule using Bayesian network structures is feasible.