Everything about Junction tree algorithm
Machine learning algorithm
The junction tree algorithm is a method used in machine learning to extract marginalization in general graphs. In essence, it entails performing belief propagation on a modified graph called a junction tree. The graph is called a tree because it branches into different sections of data; nodes of variables are the branches. The basic premise is to eliminate cycles by clustering them into single nodes. Multiple extensive classes of queries can be compiled at the same time into larger structures of data. There are different algorithms to meet specific needs and for what needs to be calculated. Inference algorithms gather new developments in the data and calculate it based on the new information provided. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Junction tree algorithmA 5-minute orientation
- Pick up a classic book: Bayesian reasoning and machine learningThe most-reprinted book on the subject
- Read the landmark paper: Lazy propagation: A junction tree inference algorithm based on lazy evaluationCited 171 times
- Go sideways: Graphical modelA closely related topic
- Go sideways: Markov random fieldA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Lazy propagation: A junction tree inference algorithm based on lazy evaluation
A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework
Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination
Discovering a junction tree behind a Markov network by a greedy algorithm
Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree
đź“š Books worth reading
Bayesian reasoning and machine learningDavid Barber · 2011- 📕Bayesian networksTimo Koski · 2009
Advances in geometric modeling and processingGMP 2010 (2010 Castro Urdiales, Spain) · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
- 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…
- 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…
- 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…
- 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…
- Chordal graph In the mathematical area of graph theory, a chordal graph is one in which all cycles of four or more vertices have a chord, which is an edge that is not…
- Graphical models for protein structure Graphical models have become powerful frameworks for protein structure prediction, protein–protein interaction, and free energy calculations for protein…
- Factor graph A factor graph is a bipartite graph representing the factorization of a function. In probability theory and its applications, factor graphs are used to…
- 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…