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46 relations: Ancestral graph, Annals of Statistics, Bayesian network, Bayesian statistics, Belief propagation, Bipartite graph, Causal inference, Clique (graph theory), Computer vision, Conditional dependence, Conditional independence, Conditional random field, Daphne Koller, David Spiegelhalter, Directed acyclic graph, Discriminative model, Factor graph, Gene regulatory network, Generative model, Graph (discrete mathematics), Graphical models for protein structure, Hidden Markov model, Information extraction, Joint probability distribution, Junction tree algorithm, Low-density parity-check code, Machine learning, Markov random field, Mixed graph, Morgan Kaufmann Publishers, Neural network, Nir Friedman, Philip Dawid, Plate notation, PLOS Computational Biology, Probability, Probability theory, Random variable, Restricted Boltzmann machine, Speech recognition, Statistical model, Statistics, Structural equation modeling, Tree (graph theory), Tree decomposition, Variable-order Markov model.
Ancestral graph
In statistics and Markov modeling, an ancestral graph is a type of mixed graph to provide a graphical representation for the result of marginalizing one or more vertices in a graphical model that takes the form of a directed acyclic graph.
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Annals of Statistics
The Annals of Statistics is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics.
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Bayesian network
A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
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Bayesian statistics
Bayesian statistics, named for Thomas Bayes (1701–1761), is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities.
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Belief propagation
Belief propagation, also known as sum-product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields.
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Bipartite graph
In the mathematical field of graph theory, a bipartite graph (or bigraph) is a graph whose vertices can be divided into two disjoint and independent sets U and V such that every edge connects a vertex in U to one in V. Vertex sets U and V are usually called the parts of the graph.
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Causal inference
Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
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Clique (graph theory)
In the mathematical area of graph theory, a clique is a subset of vertices of an undirected graph such that every two distinct vertices in the clique are adjacent; that is, its induced subgraph is complete.
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Computer vision
Computer vision is a field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
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Conditional dependence
In probability theory, conditional dependence is a relationship between two or more events that are dependent when a third event occurs.
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Conditional independence
In probability theory, two events R and B are conditionally independent given a third event Y precisely if the occurrence of R and the occurrence of B are independent events in their conditional probability distribution given Y. In other words, R and B are conditionally independent given Y if and only if, given knowledge that Y occurs, knowledge of whether R occurs provides no information on the likelihood of B occurring, and knowledge of whether B occurs provides no information on the likelihood of R occurring.
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Conditional random field
Conditional random fields (CRFs) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction.
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Daphne Koller
Daphne Koller (born August 27, 1968) is an Israeli-American Professor in the Department of Computer Science at Stanford University and a MacArthur Fellowship recipient.
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David Spiegelhalter
Sir David John Spiegelhalter, (born 16 August 1953), is a British statistician and Winton Professor of the Public Understanding of Risk in the Statistical Laboratory at the University of Cambridge and a Fellow of Churchill College, Cambridge.
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Directed acyclic graph
In mathematics and computer science, a directed acyclic graph (DAG), is a finite directed graph with no directed cycles.
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Discriminative model
Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables y on observed variables x. Within a probabilistic framework, this is done by modeling the conditional probability distribution P(y|x), which can be used for predicting y from x. Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables.
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Factor graph
A factor graph is a bipartite graph representing the factorization of a function.
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Gene regulatory network
A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins.
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Generative model
In statistical classification, including machine learning, two main approaches are called the generative approach and the discriminative approach.
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Graph (discrete mathematics)
In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related".
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Graphical models for protein structure
Graphical models have become powerful frameworks for protein structure prediction, protein–protein interaction and free energy calculations for protein structures.
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Hidden Markov model
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states.
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Information extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.
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Joint probability distribution
Given random variables X, Y,..., that are defined on a probability space, the joint probability distribution for X, Y,...
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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.
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Low-density parity-check code
In information theory, a low-density parity-check (LDPC) code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel.
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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Markov random field
In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph.
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Mixed graph
A mixed graph G.
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Morgan Kaufmann Publishers
Morgan Kaufmann Publishers is a Burlington, Massachusetts (San Francisco, California until 2008) based publisher specializing in computer science and engineering content.
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Neural network
The term neural network was traditionally used to refer to a network or circuit of neurons.
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Nir Friedman
Nir Friedman (born 1967) is an Israeli Professor of Computer Science and Biology at the Hebrew University of Jerusalem.
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Philip Dawid
Alexander Philip Dawid FRS (born 1 February 1946) is Emeritus Professor of Statistics of the University of Cambridge, and Emeritus Fellow of Darwin College, Cambridge.
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Plate notation
In Bayesian inference, plate notation is a method of representing variables that repeat in a graphical model.
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PLOS Computational Biology
PLOS Computational Biology is a peer-reviewed computational biology journal established in 2005 and published by the nonprofit Public Library of Science in association with the International Society for Computational Biology.
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Probability
Probability is the measure of the likelihood that an event will occur.
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Probability theory
Probability theory is the branch of mathematics concerned with probability.
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Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
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Restricted Boltzmann machine
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
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Speech recognition
Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.
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Statistical model
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Structural equation modeling
Structural equation modeling (SEM) includes a diverse set of mathematical models, computer algorithms, and statistical methods that fit networks of constructs to data.
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Tree (graph theory)
In mathematics, and more specifically in graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path.
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Tree decomposition
In graph theory, a tree decomposition is a mapping of a graph into a tree that can be used to define the treewidth of the graph and speed up solving certain computational problems on the graph.
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Variable-order Markov model
In stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models.
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References
[1] https://en.wikipedia.org/wiki/Graphical_model
