tDBN 0.1.4 – Polynomial-time algorithm for Learning optimal Tree-augmented Dynamic Bayesian Networks

tDBN 0.1.4

:: DESCRIPTION

tDBN is Java implementation of a dynamic Bayesian network (DBN) structure learning algorithm with the same name. It can learn a network structure from a file with multivariate longitudinal observations, and has polynomial time complexity in the number of attributes and observations.

::DEVELOPER

Systems Biomedicine

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOs/ Windows
Java

:: DOWNLOAD

tDBN

:: MORE INFORMATION

Citation

Sousa M, Carvalho AM.
Polynomial-Time Algorithm for Learning Optimal BFS-Consistent Dynamic Bayesian Networks.
Entropy (Basel). 2018 Apr 12;20(4):274. doi: 10.3390/e20040274. PMID: 33265365; PMCID: PMC7512791.

Mocapy++ 1.07 – Dynamic Bayesian Network toolkit

Mocapy++ 1.07

:: DESCRIPTION

Mocapy++ is a Dynamic Bayesian Network toolkit, implemented in C++. It supports discrete, multinomial, Gaussian, Kent, Von Mises and Poisson nodes. Inference and learning is done by Gibbs sampling/Stochastic-EM.

::DEVELOPER

The Bioinformatics Centre , University of Copenhagen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

Mocapy++

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2010 Mar 12;11:126. doi: 10.1186/1471-2105-11-126.
Mocapy++–a toolkit for inference and learning in dynamic Bayesian networks.
Paluszewski M, Hamelryck T.