Properties of k-record posteriors for the Weibull model

Bayesian inference is one of the most important issues under the model selection procedures in statistics. This paper performed a Bayesian analysis of posteriors using a well-known Weibull model with high fitting performance. We proved that there exist necessary and sufficient conditions for the priors to yield proper posteriors and finite posterior moments based on record data. Moreover, we found a significant implication through different well-known objective priors. Finally, for illustration purposes, a Monte Carlo simulation procedure is done in the paper. The results indicate that the developed inference may have a significant contribution within Bayesian analysis through applications across different areas.