Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Graphical model

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

Applications, Art & Products

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

Explore the main themes, entities and connections around Graphical model. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Types

Applications

Books and book chapters

Journal articles

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Graphical model

Nodes66
Edges65
Triples83
Avg. degree1.97
Density0.030303
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Graphical model

Top relations

related to Books and book chapters · 33
Graphical model → An, Barber, Bayesian, Bayesian Reasoning, Berlin, Bishop, CA, Cambridge University Press, Chapter, Christopher, Cowell, David, Dawid, Finn, Graphical Models, Intelligent Systems, ISBN, Judea, Lauritzen, Machine Learning
related to Journal articles · 17
Graphical model → Airoldi, Bibcode, Edoardo, Getting Started, Ghahramani, Graphical Models, Jordan, May, Nature, PLOS Computational Biology, PMC, PMID, Probabilistic, Probabilistic Graphical Models, S2CID, Statistical Science, Zoubin
related to Other types · 13
Graphical model → An, Bayesian, Boltzmann, Both, Dependency, Each, Markov, Random, TAN, Targeted Bayesian, TBNL, They, This
related to External links · 5
Graphical model → CMU, Conditional Random FieldsProbabilistic Graphical, Eric Xing, Graphical, Models
related to Types · 5
Graphical model → Bayesian, Both, Generally, Markov, Two
related to Other · 4
Graphical model → Bayesian NetworksSargur Srihari's, Graphical Models, Heckerman's Bayes Net Learning, TutorialA Brief Introduction
has application · 2
Graphical model → Applications, The
related to Cyclic Directed Graphical Models · 2
Graphical model → The, This

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

graph graphical model bayesian undirected models random networks directed probabilistic learning network probability machine isbn conditional variables markov joint structure

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
variable-order Markov models can be considered special cases of Bayesian networks.One of the simplest Bayesian Networks is the Naive Bayes classifier.Cyclic Directed Graphical ModelsThe next figure depicts a graphical model with a cycleinstance ofneural networks and newer models0.80text
variable-order Markov models can be considered special cases of Bayesian networks.One of the simplest Bayesian Networks is the Naive Bayes classifierinstance ofneural networks and newer models0.80text
Graphical modelhas applicationThe0.60section
Graphical modelhas applicationApplications0.60section
Graphical modelrelated to Books and book chaptersBarber0.60section
Graphical modelrelated to Books and book chaptersDavid0.60section
Graphical modelrelated to Books and book chaptersBayesian Reasoning0.60section
Graphical modelrelated to Books and book chaptersMachine Learning0.60section
Graphical modelrelated to Books and book chaptersCambridge University Press0.60section
Graphical modelrelated to Books and book chaptersISBN0.60section
Graphical modelrelated to Books and book chaptersBishop0.60section
Graphical modelrelated to Books and book chaptersChristopher0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.