389 citations · 2.3k across the 33 of their papers we have counts for
7 papers · 1 filter
Staged Mixture Modelling and Boosting
Christopher Meek, Bo Thiesson, David Heckerman
In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to seque…
Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or…
Finding Optimal Bayesian Networks
David Maxwell Chickering, Christopher Meek
In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scor…
Practically Perfect
Christopher Meek, David Maxwell Chickering
The property of perfectness plays an important role in the theory of Bayesian networks. First, the existence of perfect distributions for arbitrary sets of variables and directed a…
Large-Sample Learning of Bayesian Networks is NP-Hard
David Maxwell Chickering, Christopher Meek, David Heckerman
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a s…
ARMA Time-Series Modeling with Graphical Models
Bo Thiesson, David Maxwell Chickering, David Heckerman +1
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it effectively impossible t…