T.J. Loredo. From Laplace to Supernova SN 1987. Home/Search Document Not in Database Summary Related Articles Check |
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Bayesian Probability - Bruyninckx (2002) (Correct)
....entropy of the distribution taking into account the constraints. For example, if one knows the mean and variance of a parameter, the probability distribution that adds no extra a priori information (and hence has the largest entropy) turns out to be the Gaussian with given mean and variance, e.g. [3, 18, 24, 25]. 3. Extra I don t know hypothesis. If the robot has a set of hypotheses for the system under consideration, and it doesn t know at all which one to prefer, or even whether one of these hypotheses is valid, it can add a new hypothesis that just says I don t know what hypothesis is valid. ....
T. J. Loredo. From Laplace to supernova SN 1987.
Minimum Message Length Inference: Theory and Applications - Baxter (1996) (2 citations) (Correct)
.... types of loss functions are commonly identified: A 0 1 loss function: l(#, #) is a ball of radius # centered at # [17, Page 257] A quadratic loss function: l(#, # # ) A linear loss function: l(#, # These three loss functions lead to the following estimates [87]: 1 loss Mode. The value which maximises the posterior density. # = max(P osterior(#) Quadratic loss Mean. #in# P osterior(#) # d# . Linear loss Median. The value of # which satisfies: P osterior(#) d# = P osterior(#) d# Where the parameter to be ....
T.J. Loredo. From Laplace to Supernova SN 1987.
MML and Bayesianism: Similarities and Differences.. - Oliver, Baxter (1994) (18 citations) (Correct)
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T.J. Loredo. From Laplace to Supernova SN 1987.
Chapter 12 - Partial-Volume Bayesian.. (2001) (Correct)
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T. J. Loredo, "From Laplace to supernova SN1987.
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