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Bayesian statistics and parameter estimation

We propose a mathematical relation, which maps the predictors x to the responses that involves a set of adjustable model parameters 0 e 9t, whose values we wish to estimate from the measured response data. Let us say that we have a set of N experiments, in which for experiment k=, 2. N, is the row vector of predictor variables and the row vector of measured response data isyf f For each experiment, we have a model prediction of the response [Pg.372]

The basic regression problem is given a proposed model how do we choose [Pg.373]

9 such that the model predictions agree most closely to die observations Of [Pg.373]


See other pages where Bayesian statistics and parameter estimation is mentioned: [Pg.372]    [Pg.374]    [Pg.376]    [Pg.378]    [Pg.380]    [Pg.382]    [Pg.384]    [Pg.386]    [Pg.388]    [Pg.390]    [Pg.392]    [Pg.394]    [Pg.396]    [Pg.398]    [Pg.400]    [Pg.402]    [Pg.404]    [Pg.406]    [Pg.408]    [Pg.410]    [Pg.412]    [Pg.414]    [Pg.416]    [Pg.418]    [Pg.420]    [Pg.422]    [Pg.424]    [Pg.426]    [Pg.428]    [Pg.430]    [Pg.432]    [Pg.434]    [Pg.477]   


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