Deal: A package for learning bayesian networks

94Citations
Citations of this article
158Readers
Mendeley users who have this article in their library.

Abstract

deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

References Powered by Scopus

R: A Language for Data Analysis and Graphics

9060Citations
N/AReaders
Get full text

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

2698Citations
N/AReaders
Get full text

Optimal structure identification with greedy search

1309Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Learning Bayesian networks with the bnlearn R Package

1178Citations
N/AReaders
Get full text

Bayesian Networks in R: with Applications in Systems Biology

281Citations
N/AReaders
Get full text

A survey on Bayesian network structure learning from data

193Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Bøttcher, S. G., & Dethlefsen, C. (2003). Deal: A package for learning bayesian networks. Journal of Statistical Software, 8, 1–19. https://doi.org/10.18637/jss.v008.i20

Readers over time

‘09‘10‘11‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘2407142128

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 72

53%

Researcher 37

27%

Professor / Associate Prof. 28

20%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 33

36%

Computer Science 32

35%

Engineering 18

20%

Mathematics 9

10%

Save time finding and organizing research with Mendeley

Sign up for free
0