Approximations of unsupervised Bayes learning procedures

Makov, Udi

doi:10.1007/bf02888347

Cited by 3 publications

(2 citation statements)

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“…for the next k observations using (3)-{1O) and so on. The exact evaluation of the mean and variance of the mixture is computationally too cumbersome for large k, Quasi-Bayes (QB(k» (Makov, 1979;Smith and Makov, 1980) Here the unknown o's are substituted by their expectation.…”

Section: Approximating Proceduresmentioning

confidence: 99%

Approximate Bayesian Procedures for Dynamic Linear Models in the Presence of Jumps

Makov

1983

The Statistician

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Section: Approximating Proceduresmentioning

confidence: 99%

Approximate Bayesian Procedures for Dynamic Linear Models in the Presence of Jumps

Makov

1983

The Statistician

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“…PROFILE LIKELIHOOD WITH SOME RANDOM X Suppose ( Yi, Xi), for i = 1, . The situation is akin to unsupervized learning as discussed, for example, by Makov (1980). ., n, are calibration data from model (I i 1) with controlled X's, but that there are also separately a random sample of X-values, px 1 vectors x,...…”

Section: Introductionmentioning

confidence: 99%

Prediction Diagnostic and Updating in Multivariate Calibration

Brown¹,

Sundberg²

1989

Biometrika

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARYIn multivariate calibration the relationship between a q-variate response vector Y and p explanatory variables X are estimated from training data in order to predict an unknown X, denoted by e, from further observed responses. When q > p both the profile likelihood for e and Bayesian inference for e depend on a prediction inconsistency diagnostic which highlights those response vectors used for prediction which are internally inconsistent in the prediction of e. When several further response vectors together display systematic anomalies one is led to questioning the estimated model. The information in prediction data about changes in parameters is investigated under various assumptions. The results indicate systematic anomalies in prediction data may be detected in a variety of ways, but corrected only under strong assumptions so that recalibration might be needed.

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Bayesian and approximate bayesian solutions to simultaneous estimation of multiple dynamic processes

Makov

1991

Communications in Statistics - Theory and Methods

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Approximations of unsupervised Bayes learning procedures

Cited by 3 publications

References 22 publications

Approximate Bayesian Procedures for Dynamic Linear Models in the Presence of Jumps

Approximate Bayesian Procedures for Dynamic Linear Models in the Presence of Jumps

Prediction Diagnostic and Updating in Multivariate Calibration

Bayesian and approximate bayesian solutions to simultaneous estimation of multiple dynamic processes

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