Collection of open machine learning papers

Bayesian network 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) (Wiki

**Overview**- Introducing Bayesian Networks (2004) - free chapter from the Bayesian Artificial Intelligence book
*Kevin B. Korb, Ann E. Nicholson* - A Tutorial on Learning With Bayesian Networks (2020)
*David Heckerman*

- Introducing Bayesian Networks (2004) - free chapter from the Bayesian Artificial Intelligence book

**Discrete Bayesian Networks**- Distributions are assumed to be multinomial, represented by tables

**Gaussian Bayesian Networks**- Distributions are normal
- Learning Gaussian Networks (1994)
*Dan Geiger, David Heckerman*

**Mixed**- Bayesian networks with variables that have different distributions
- Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative (1989)
*S. L. Lauritzen, N. Wermuth* - Copula Bayesian Networks (2010)
*Gal Elidan*

**Dynamic Bayesian Networks**- Relates variables to each other over some time steps
- Dynamic Network Models for Forecasting (1992)
*Paul Dagum, Adam Galper, Eric Horvitz* - Dynamic Bayesian Networks: Representation, Inference and Learning (2002)
*Kevin Patrick Murphy*

Learning the graph structure that represents the conditional independencies between variables. Main approaches are *constraint-based* (conditional independence tests) and *score-based* (goodness-of-fit scores)

**Inductive Causation**- Equivalence and Synthesis of Causal Models (1990)
*TS Verma, Judea Pearl*

- Equivalence and Synthesis of Causal Models (1990)
**Sparse Candidate**- Learning Bayesian Network Structure from Massive Datasets: The “Sparse Candidate” Algorithm (1999)
*Nir Friedman, Iftach Nachman, Dana Peer*

- Learning Bayesian Network Structure from Massive Datasets: The “Sparse Candidate” Algorithm (1999)
**Greedy Search**- Optimal Structure Identification With Greedy Search (2002)
*David M. Chickering* - Learning Bayesian Networks with Thousands of Variables (2015)
*Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon* - Learning Bayesian networks from big data with greedy search:computational complexity and efficient implementation (2019)
*Marco Scutari, Claudia Vitolo, Allan Tucker*

- Optimal Structure Identification With Greedy Search (2002)
**Grow-Shrink**- Learning Bayesian Network Model Structure from Data (Ph.D. thesis, 2003)
*Dimitris Margaritis*

- Learning Bayesian Network Model Structure from Data (Ph.D. thesis, 2003)
**Incremental Association**- Algorithms for Large Scale Markov Blanket Discovery (2003)
*Ioannis Tsamardinos, Constantin F. Aliferis, Alexander Statnikov* - Interleaved Incremental Association
- Fast Incremental Association

Speculative Markov BlanketDiscoveryforOptimalFeature Selection (2005)*Sandeep Yaramakala, Dimitris Margaritis*

- Algorithms for Large Scale Markov Blanket Discovery (2003)
**Optimal Reinsertion**- Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning (2003)
*Andrew Moore, Weng-Keen Wong*

- Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning (2003)
**Max-Min Parents and Children**- The max-min hill-climbing Bayesian network structure learning algorithm (2006)
*Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis*

- The max-min hill-climbing Bayesian network structure learning algorithm (2006)
**Other**- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (1995)
*D. Heckerman, D.Geiger, D. M. Chickering*

- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (1995)

Estimation of the parameters of the global distribution with known graph structure.

- Learning Bayesian network parameters under incomplete data with domain knowledge (2009)
*Wenhui Liaoa, Qiang Ji* **MLE**Maximum Likelihood Estimate**Bayesian method****EM**Expectation-maximization**RBE**Robust Bayesian Estimate**Monte-Carlo Method****Gaussian approximation**