A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection (1996) [15 citations — 0 self]
by
David Heckerman
,
David Maxwell Chickering
Statistics and Computing
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@TECHREPORT{Heckerman96acomparison,
author = {David Heckerman and David Maxwell Chickering},
title = {A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection},
institution = {Statistics and Computing},
year = {1996}
}
author = {David Heckerman and David Maxwell Chickering},
title = {A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection},
institution = {Statistics and Computing},
year = {1996}
}
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Abstract:
this paper, we assume that there are a finite number of possible true models. For each possible model m, we define the random (vector) variable \Theta m whose values correspond to the possible values of the parameters for m. We encode our uncertainty about \Theta m using the probability distribution p(\Theta m jm). In this paper, we assume that p(\Theta m jm) is a probability density function. Given random sample D, we compute the posterior distributions for M and each \Theta m
Citations
| 974 | Estimating the dimension of a model – Schwarz - 1978 |
| 727 | A bayesian method for the induction of probabilistic networks from data – Cooper, Hersovits - 1992 |
| 507 | Information theory and an extension of the maximum likelihood principle – Akaike - 1973 |
| 95 | Causal diagrams for empirical research – Pearl - 1995 |
| 61 | Assessment and propagation of model uncertainty – Draper - 1995 |
| 43 | Expected Information as Expected Utility – Bernardo - 1979 |
| 30 | Present Position and Potential Developments: Some Personal Views - Statistical Theory, The Prequential Approach – Dawid - 1984 |
| 30 | Bayesian model averaging – Hoeting, Maigan, et al. - 1999 |
| 13 | A comparison of the information and posterior probability criteria for model selection – Chow - 1981 |
| 7 | A Predictive Model Selection Criterion – Martini, A, et al. - 1984 |
