factors.rst

Factors module

The factors are usually represented as conditional probability functions and are a component of a Bayesian network.

Abstract Types

The FactorType <pybnesian.FactorType> and Factor <pybnesian.Factor> classes are abstract and both of them need to be implemented to create a new factor type. Each Factor <pybnesian.Factor> is always associated with a specific FactorType <pybnesian.FactorType>.

pybnesian.FactorType

pybnesian.Factor

Continuous Factors

Linear Gaussian CPD

pybnesian.LinearGaussianCPDType

pybnesian.LinearGaussianCPD

Conditional Kernel Density Estimation (CKDE)

pybnesian.CKDEType

pybnesian.CKDE

Discrete Factors

pybnesian.DiscreteFactorType

pybnesian.DiscreteFactor

Hybrid Factors

pybnesian.CLinearGaussianCPD

pybnesian.HCKDE

Other Types

This types are not factors, but are auxiliary types for other factors.

Kernel Density Estimation

pybnesian.BandwidthSelector

pybnesian.ScottsBandwidth

pybnesian.NormalReferenceRule

pybnesian.UCV

pybnesian.KDE

pybnesian.ProductKDE

pybnesian.SingularCovarianceData

This exception signals that the data has a singular covariance matrix.

Other

pybnesian.UnknownFactorType

pybnesian.Assignment

pybnesian.Args

pybnesian.Kwargs

pybnesian.Arguments

Bibliography

HybridSemiparametric

David Atienza and Pedro Larrañaga and Concha Bielza. Hybrid semiparametric Bayesian networks. TEST, vol. 31, pp. 299-327, 2022.

MVKSA

José E. Chacón and Tarn Duong. (2018). Multivariate Kernel Smoothing and Its Applications. CRC Press.

Scott

Scott, D. W. (2015). Multivariate Density Estimation: Theory, Practice and Visualization. 2nd Edition. Wiley

Semiparametric

David Atienza and Concha Bielza and Pedro Larrañaga. Semiparametric Bayesian networks. Information Sciences, vol. 584, pp. 564-582, 2022.