Bayesian Evaluation of Informative Hypotheses Dissertation by Herbert Hoijtink

Herbert Hoijtink's dissertation from Utrecht University develops approximate Bayesian methods for evaluating informative hypotheses using Bayes factors. The work proposes default prior specification methods and uses normal approximations for posterior distributions in a general class of statistical models. Two software packages were developed to compute the Bayes factor.

Use Cases

• Comparing statistical hypotheses based on Bayes factor calculations described in the dissertation.
• Applying default prior specification methods for Bayesian model evaluation in general statistical models.
• Using the developed software packages for computing Bayes factors in research workflows.

Strengths

• Developed by a named author (Herbert Hoijtink) from a specific institution (Utrecht University).
• Includes two software packages for computation, as stated in the description.
• Focuses on a general class of statistical models, suggesting broad applicability.

Limitations

• Row count, column names, and sample data are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Herbert Hoijtink, Utrecht University

Collection Method

Dissertation research developing statistical methods and software.