Bayesian Graphical Models for Discrete Data

By David Madigan, Jeremy York. 1995

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Giudici and Castelo (2003) use a similar construction to efﬁciently obtain the legal actions their MCMC sampler may take.”
DAG-GNN: DAG Structure Learning with Graph Neural Networks (2019, background): “, 1999) and sampling-based structure learning algorithms (Madigan et al., 1995; Friedman & Koller, 2003; Eaton & Murphy, 2012; Grzegorczyk & Husmeier, 2008; Niinimäki & Koivisto, 2013; Niinimaki et al., 2012; He et al., 2016) are also proposed to tackle the expensive search problem.”
Nonparametric Convergence Assessment for MCMC Model Selection (2003, background): “See, for instance, the articles by Madigan and Raftery (1994), Madigan and York (1995), Geiger and Heckerman (1994), and Giudici and Green (1999).”
Partition MCMC for Inference on Acyclic Digraphs (2015, methodology): “With this article, we provide R code for structure (Madigan and York 1995) and order (Friedman and Koller 2003) MCMC, together with the algorithm of Grzegorczyk and Husmeier (2008) and our newly proposed partition MCMC.”
Mixtures of g-priors for Bayesian model averaging with economic applications (2010, unknown): “A convenient way to do this is through Markov chain Monte Carlo, in particular the MC3 algorithm of Madigan and York (1995) has been used in this context with success (Fernández et al., 2001a; and Ley and Steel, 2009).”
Learning Object Affordances: From Sensory--Motor Coordination to Imitation (2008, unknown): “Markov Chain Monte Carlo (MCMC) methods have been proposed to approximate the distribution p(G | D) [29].”
Tree inference for single-cell data (2016, unknown): “Akin to standard MCMC approaches on graphical structures [41], we build a scheme on rooted mutation trees for fixed errors θ as follows.”
The Geography of Output Volatility (2009, unknown): “[71] Madigan, D.”
Bayesian Variable and Transformation Selection in Linear Regression (2002, methodology): “To address this problem, we have adapted the Markov chain Monte Carlo model composition (MC3) approach of Madigan and York (1995) to do BMA over the space of all variables and transformations.”
Towards Scalable Bayesian Learning of Causal DAGs (2020, methodology): “The first methods simulated a Markov chain on the space of DAGs by applying edge operations (add, remove, and reverse edge), yielding a sample of DAGs approximately from the posterior [24, 11].”
DiBS: Differentiable Bayesian Structure Learning (2021, methodology): “Structure MCMC (MC(3)) [36, 37] performs Metropolis-Hastings in the space of DAGs by changing one edge at a time.”
Trade openness and economic growth : a cross-country empirical investigation (2008, methodology): “For this, we follow Madigan and York (1995) and define a neighbourhood for each possible model Mj, nbd(M) which consists of the models including the same growth theories as well as the one more and less theories with respect to the model Mj.”
Foundations of Linear and Generalized Linear Models (2015, background): “4 Proportional odds: Although not the first to use the proportional odds form of cumulative logit model, the landmark article on modeling ordinal data by McCullagh (1980) popularized it. Peterson and Harrell (1990) proposed a partial proportional odds model in which a subset of the explanatory varia”