The complexity of Bayesian networks specified by propositional and relational languages

Cozman, Fábio Gagliardi; Mauá, Denis Deratani

doi:10.1016/j.artint.2018.06.001

Cited by 7 publications

(2 citation statements)

References 89 publications

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“…This leads to several inconveniences. We make use of an adapted version of weighted reduction [13], as suggested in [21], but we allow for affine postprocessing of the output from the desired function. For other definitions of metric reductions, see for example [28].…”

Section: Weighted Counting Problemsmentioning

confidence: 99%

A Structured View on Weighted Counting with Relations to Counting, Quantum Computation and Applications

Campos¹,

Stamoulis²,

Weyland³

2017

Preprint

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Weighted counting problems are a natural generalization of counting problems where a weight is associated with every computational path of polynomial-time non-deterministic Turing machines and the goal is to compute the sum of the weights of all paths (instead of just computing the number of accepting paths). Useful closure properties and plenty of applications make weighted counting problems interesting. The general definition of these problems captures even undecidable problems, but it turns out that obtaining an exponentially small additive approximation is just as hard as solving conventional counting problems. In many cases such an approximation is sufficient and working with weighted counting problems tends to be very convenient. We present a structured view on weighted counting by defining classes that depend on the range of the function that assigns weights to paths and by showing the relationships between these different classes. These classes constitute generalizations of the usual counting problems. Weighted counting allows us to easily cast a number of famous results of computational complexity in its terms, especially regarding counting and quantum computation. Moreover, these classes are flexible enough and capture the complexity of various problems in fields such as probabilistic graphical models and stochastic combinatorial optimization. Using the weighted counting terminology and our results, we are able to simplify and answer some open questions in those fields.

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Section: Weighted Counting Problemsmentioning

confidence: 99%

A Structured View on Weighted Counting with Relations to Counting, Quantum Computation and Applications

Campos¹,

Stamoulis²,

Weyland³

2017

Preprint

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“…In certain cases, its structures also rely on prior knowledge to realize non-data-type information fusion [19] [20]. In addition, parallel computing frameworks have been introduced to accelerate the learning rate of the BNs model in cases related to big data environments [21] [22]. BNs provide a complex description of interrelated risk and model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger

Romahadi

Anggara²,

Youlia³

et al. 2022

Sinergi

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The heat exchanger highly influences the series of cooling processes. Therefore, it is required to have maximum performance. Some of the factors causing a decrease in its performance are increased pressure drop in the Plate Heat Exchanger (PHE), decreased output flow, leakage, flow obstruction, and mixing of fluids. Furthermore, it takes a long time to conclude the diagnosis of the performance and locate the fault. Therefore, this study aims to design an intelligent system for the performance diagnosis of the PHE using the Bayesian Networks (BNs) method approach. BNs are applied to new problems that require a new BNs network model. The system was designed using MSBNX and MATLAB software, comprising several implementation stages. It starts by determining the related variables and categories in the network, making a causality diagram, determining the prior probability of the variable, filling in the conditional probability of each variable, and entering evidence to analyze the prediction results. This is followed by carrying out a case test on the maintenance history to display the probability inference that occurs during pressure drop on the PHE. The result showed that the BNs method was successfully applied in diagnosing the PHE. When there is evidence of input in the form of a pressure drop, the probability value of non-conforming pressure-flow becomes 61.12%, PHE clogged at 73.59%, and actions to clean pipes of 70.18%. In conclusion, the diagnosis carried out by the system showed accurate results.

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The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws

Cozman

Mauá

2019

International Journal of Approximate Reasoning

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The complexity of Bayesian networks specified by propositional and relational languages

Cited by 7 publications

References 89 publications

A Structured View on Weighted Counting with Relations to Counting, Quantum Computation and Applications

A Structured View on Weighted Counting with Relations to Counting, Quantum Computation and Applications

Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger

The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws

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