Probabilistic graphical models : principles and techniques

By Daniel L. Koller, Nir Friedman. 2009

297 citations recorded by the source corpus.

Citations in the corpus

UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “This is a simple version of tabu search (e.g. Koller and Friedman, 2009) that is used to avoid local optima.”
Neural Networks and Deep Learning (2018, unknown): “These methods are particularly useful for creating generative models of the data, and they are closely related to probabilistic graphical models [251].”
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (2011, unknown): “Since (Σ−1)ij is zero if and only if the ith and jth components of the random variable are conditionally independent, given the other variables, this problem is equivalent to the structure learning problem of estimating the topology of the undirected graphical model representation of the Gaussian [1”
Minimax Estimation of Functionals of Discrete Distributions (2014, unknown): “tatistics as a tool for dimensionality reduction, classiﬁcation, and as a foundation for algorithm design in more complex dependence structures [183] in the theory of learning graphical models [184], [185]. It has also been widely adopted in applied research, and is particularly popular in systems b”
Generalized Network Psychometrics: Combining Network and Latent Variable Models (2016, unknown): “Further equivalences and differences between GGMs and DAGs are beyond the scope of this paper, but haven been well described in the literature (e.g., chapter 3 of Lauritzen 1996; Koller and Friedman 2009; Kolaczyk 2009).”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, unknown): “A useful alternate notation describes V in terms of a graph [Koller and Friedman, 2009].”