Bayesian imperfect information analysis for clinical recurrent data

Bayesian imperfect information analysis for clinical recurrent data

In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our met...

Bayesian Opponent Exploitation in Imperfect-Information Games

Two fundamental problems in computational game theory are computing a Nash equilibrium and learning to exploit opponents given observations of their play (opponent exploitation). The latter is perhaps even more important than the former: Nash equilibrium does not have a compelling theoretical justification in game classes other than two-player zero-sum, and for all games one can potentially do ...

Bayesian Prediction in Spatial Data Analysis

Bayesian Decision Analysis for Recurrent Ovarian Cancer

All cancers are usually classified further according to the extent or stage of disease so that therapies may be tailored to the particular disease stage. Moreover, detection of asymptomatic recurrences is associated with prolonged overall survival and survival from the time of initial detection of recurrence. This study applied Bayesian decision theorem to an inferential problem of recurrent ov...

Bayesian Decision Analysis for Recurrent Cervical Cancer

Risk modeling for recurrent cervical cancer requires the development of new concepts and methodologies. Unlike most daily decisions, many medical decision making have substantial consequences, and involve important uncertainties and trade-offs. The uncertainties may be about the accuracy of available diagnostic tests, the natural history of the cervical cancer, the effects of treatment in a pat...