
DeepLab
 Referenced in 20 articles
[sw15303]
 combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of maxpooling...

PMTK
 Referenced in 181 articles
[sw14689]
 Kevin Murphy’s textbook Machine learning: a probabilistic perspective, but can also be used independently ... software framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods...

hglasso
 Referenced in 10 articles
[sw11202]
 problem of learning a highdimensional graphical model in which there ... general framework to three widely used probabilistic graphical models: the Gaussian graphical model, the covariance...

Venture
 Referenced in 9 articles
[sw14670]
 models and inference problems in Venture are specified via a Turingcomplete, higherorder probabilistic ... implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure ... supports custom control flow, higherorder probabilistic procedures, partially exchangeable sequences and “likelihoodfree” stochastic...

REBA
 Referenced in 3 articles
[sw29435]
 complementary strengths of declarative programming and probabilistic graphical models to represent and reason with...

foxPSL
 Referenced in 2 articles
[sw13725]
 usually by combining logical representations with probabilistic graphical models. PSL can be seen as both...

Belief
 Referenced in 1 article
[sw14242]
 BELIEF: Graphical Belief Function Models and Graphical Probabilistic Models. BELIEF is a Common Lisp implementation ... Kong fusion and propagation algorithm for Graphical Belief Function Models ... Lauritzen and Spiegelhalter algorithm for Graphical Probabilistic Models. It includes code for manipulating graphical belief...

OpenMarkov
 Referenced in 2 articles
[sw14326]
 OpenMarkov is a software tool for probabilistic graphical models (PGMs) developed by the Research Centre...

PyGModels
 Referenced in 1 article
[sw38510]
 PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures. Probabilistic Graphical...

FACTORIE
 Referenced in 13 articles
[sw08947]
 Factorie: Probabilistic programming via imperatively defined factor graphs. Discriminatively trained undirected graphical models have...

Pgmpy
 Referenced in 1 article
[sw38511]
 Pgmpy: Probabilistic graphical models using python...

stagedtrees
 Referenced in 1 article
[sw32870]
 probability models. Staged event trees are probabilistic graphical models capable of representing asymmetric conditional independence...

pyGM
 Referenced in 1 article
[sw38512]
 simple Pythonbased interface for defining probabilistic graphical models (Bayesian networks, factor graphs, etc.) over...

abn
 Referenced in 1 article
[sw31036]
 network analysis is a form of probabilistic graphical models which derives from empirical data...

Pgm
 Referenced in 1 article
[sw38514]
 Probabilistic graphical models in python. This code is intended mainly as proof of concept...

JSNice
 Referenced in 1 article
[sw37814]
 from existing data and then uses this model to predict properties of new, unseen programs ... formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs...

simPATHy
 Referenced in 1 article
[sw23961]
 approach is built upon probabilistic graphical models and is thus especially suited for testing topological...

tsBNgen
 Referenced in 1 article
[sw35076]
 Bayesian networks are a type of probabilistic graphical model widely used to model the uncertainties...

ReactomeFIViz
 Referenced in 1 article
[sw25625]
 types into a pathway context using probabilistic graphical models. We believe our app will give...

UGM
 Referenced in 1 article
[sw28257]
 functions implementing various tasks in probabilistic undirected graphical models of discrete data with pairwise...