Search results for "Lindley's paradox"

showing 2 items of 2 documents

Bayesian hypothesis testing: A reference approach

2002

Summary For any probability model M={p(x|θ, ω), θeΘ, ωeΩ} assumed to describe the probabilistic behaviour of data xeX, it is argued that testing whether or not the available data are compatible with the hypothesis H0={θ=θ0} is best considered as a formal decision problem on whether to use (a0), or not to use (a0), the simpler probability model (or null model) M0={p(x|θ0, ω), ωeΩ}, where the loss difference L(a0, θ, ω) –L(a0, θ, ω) is proportional to the amount of information δ(θ0, ω), which would be lost if the simplified model M0 were used as a proxy for the assumed model M. For any prior distribution π(θ, ω), the appropriate normative solution is obtained by rejecting the null model M0 wh…

CombinatoricsBinomial distributionStatistics and ProbabilityBayes' theoremDistribution (mathematics)Prior probabilityStatisticsMultivariate normal distributionContext (language use)Statistics Probability and UncertaintyLindley's paradoxMathematicsStatistical hypothesis testing
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A Bayesian analysis of classical hypothesis testing

1980

The procedure of maximizing the missing information is applied to derive reference posterior probabilities for null hypotheses. The results shed further light on Lindley’s paradox and suggest that a Bayesian interpretation of classical hypothesis testing is possible by providing a one-to-one approximate relationship between significance levels and posterior probabilities.

Statistics and ProbabilityBayes factorBayesian inferenceStatistics::ComputationBayesian statisticsStatisticsEconometricsBayesian experimental designStatistics::MethodologyStatistics Probability and UncertaintyBayesian linear regressionLindley's paradoxBayesian averageMathematicsStatistical hypothesis testingTrabajos de Estadistica Y de Investigacion Operativa
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