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Everything about Graphical model
Probabilistic 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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Graphical modelA 5-minute orientation
- Skim a recent paper: Feature Matching via Motion-Consistency Driven Probabilistic Graphical ModelInfluential work since 2022
- Pick up a classic book: Mathematics with applicationsThe most-reprinted book on the subject
- Read the landmark paper: Coot : model-building tools for molecular graphicsCited 30,014 times
- Go sideways: Bayesian networkA closely related topic
- Go sideways: Dependency network (graphical model)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Coot : model-building tools for molecular graphics
Model selection and estimation in the Gaussian graphical model
A Cognitive Model for Understanding Graphical Perception
Decomposable graphical Gaussian model determination
Probabilistic Graphical Model Representation in Phylogenetics
Graphical templates for model registration
Graphical model architectures for speech recognition
Joint Gaussian graphical model estimation: A survey
🆕 Influential since 2022
Feature Matching via Motion-Consistency Driven Probabilistic Graphical Model
Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations
High-dimensional functional graphical model structure learning via neighborhood selection approach
DSMCompare: domain-specific model differencing for graphical domain-specific languages
Frequentist Model Averaging for Undirected Gaussian Graphical Models
📚 Books worth reading
Mathematics with applicationsMargaret L. Lial, Thomas W. Hungerford · 1974
The Psychology of Everyday ThingsDonald A. Norman · 1988
Elementary mathematical modelingMary Ellen Davis, C. Edwards · 2000
Autodesk Inventor from the Top (Autodesk Inventor)Daniel T. Banach · 2001- 📕Empirical Model BuildingThompson, James R. · 2008
- 📕Computer-aided geometry modelingJohn N. Shoosmith, Robert E. Fulton · 1984
Paradise Kiss 4矢沢あい · 2002- 📕HypergraphicsDavid Brisson · 2019
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Bayesian network A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a…
- Dependency network (graphical model) Dependency networks (DNs) are graphical models, similar to Markov networks, wherein each vertex (node) corresponds to a random variable and each edge captures…
- Markov random field In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having…
- Graphoid A graphoid is a set of statements of the form, "X is irrelevant to Y given that we know Z" where X, Y and Z are sets of variables. The notion of "irrelevance"…
- Conditional random field In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction…
- Ancestral graph In statistics and Markov modeling, an ancestral graph is a type of mixed graph used to provide a graphical representation for the result of marginalizing…
- Junction tree algorithm The junction tree algorithm (also known as 'Clique Tree') is a method used in machine learning to extract marginalization in general graphs. In essence…
- Moral graph In graph theory, a moral graph is used to find the equivalent undirected form of a directed acyclic graph. It is a key step of the junction tree algorithm…