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Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model… (More)

- David Madigan
- Networks
- 1994

- Alexander Genkin, David D. Lewis, David Madigan
- Technometrics
- 2007

Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid overfitting and produces sparse predictive models for text data. We… (More)

- David Madigan, Adrian E. Raftery, +4 authors Nanny Wermuth
- 1993

We consider the problem of model selection and accounting for model uncertainty in high dimensional contingency tables motivated by expert system applications The approach most used currently is a stepwise strategy guided by tests based on approxi mate asymptotic P values leading to the selection of a single model inference is then conditional on the… (More)

We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e.,… (More)

Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dependences among statistical variables. Applications of undirected graphs (UGs) include models for spatial dependence and image analysis, while acyclic directed graphs (ADGs), which are especially convenient for statistical analysis, arise in such fields as… (More)

- David Madigan, E. Einahrawy, R. P. Martin, Wen-Hua Ju, Parameshwaran Krishnan, A. S. Krishnakumar
- Proceedings IEEE 24th Annual Joint Conference of…
- 2005

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and… (More)

- Dmitriy Fradkin, David Madigan
- KDD
- 2003

Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computational advantages. In this paper we report a number of experiments to evaluate Random Projections in the context of inductive supervised learning. In particular, we compare Random… (More)

Semi-supervised learning is by no means an unfamiliar concept to natural language processing researchers. Labeled data has been used to improve unsupervised parameter estimation procedures such as the EM algorithm and its variants since the beginning of the statistical revolution in NLP (e.g., Pereira and Schabes (1992)). Unlabeled data has also been used… (More)

We consider the problems of variable selection and accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. The complete Bayesian solution to this problem involves averaging over… (More)