Sentences with phrase «exact probability matching»

Fortunately, in one - dimensional cases where uncertainty involves measurement and similar errors it is often possible to find a completely noninformative prior, with the result that exact probability matching can be achieved.

Demonstrating that Bayesian inference using Jeffreys prior and inference using likelihood ratios gives exact probability matching and hence accurate CIs, whereas subjective Bayesian methods don't except in a special, unrealistic, case, shakes them up a bit and hopefully makes them think again.

But the simplified case presented here falls within an exception to that rule, and use of Jeffreys» prior should in principle lead to exact probability matching.

By definition, an accurate confidence interval exhibits perfect frequentist coverage and so represents, for an x % interval, exact probability matching.

How do the SRLR and objective Bayesian methods provide exact probability matching for each individual calendar date?

Since they provide exact probability matching for each individual calendar date, they are bound to provide exact probability matching whatever probability distribution for calendar date is assumed by the drawing of samples.

Jeffreys» prior would in fact provide exact probability matching — perfect agreement between the objective Bayesian posterior cumulative distribution functions (CDFs — the integrals of PDFs) and the results of repeated testing.

The key point here is that the objective Bayesian and the SRLR methods both provide exact probability matching whatever the true calendar date of the sample is (provided it is not near the end of the calibration curve).

By contrast, both an objective Bayesian method using Jeffreys» prior and the SRLR method will provide exact probability matching whatever distribution of sample ages the process that actually generated the sample produces.

Not exact matches

For both variants of the uniform prior subjective Bayesian method, probability matching is nothing like exact except in the unrealistic case where the sample is drawn equally from the entire calibration range — in which case over-coverage errors in some regions on average cancel out with under - coverage errors in other regions, probably reflecting the near symmetrical form of the stylised overall calibration curve.