Bayesian posterior repartitioning for nested sampling

Understand

Priors in Bayesian analyses often encode informative domain knowledge that can be useful in making the inference process more efficient.

• Occasionally, however, priors may be unrepresentative of the parameter values for a given dataset, which can result in inefficient parameter space exploration, or even incorrect inferences, particularly for nested sampling (NS) algorithms.
• Simply broadening the prior in such cases may be inappropriate or impossible in some applications.
• Hence our previous solution to this problem, known as posterior repartitioning (PR), redefines the prior and likelihood while keeping their product fixed, so that the posterior inferences and evidence estimates remain unchanged, but the efficiency of the NS process is significantly increased.