Some empirical and theoretical studies have emphasized on fixed exchange rate regime in controlling time inconsistency, while others consider the role of target zone regime as an important factor. Thus there is no general consensus to decide which exchange rate regime may bring about less time-inconsistency. The main purpose of this study is to investigate policy time-inconsistency in exchange ...

This chapter presents general Bayesian concepts and some specific designs for human clinical trials of targeted agents. The designs employ decision rules that use each patient’s protein or gene expression biomarkers, and possibly conventional prognostic variables, to choose an individualized treatment regime that may include one or several targeted agents. The Bayesian rules are sequentially ad...

In recent years, the importance of artificial intelligence (AI) and reinforcement learning (RL) has exponentially increased in healthcare Dynamic Treatment Regimes (DTR). These techniques are used to learn recover best doctor’s treatment policies. However, methods based on existing RL approaches encountered with some limitations e.g. behavior cloning (BC) suffer from compounding errors use self...

We propose a regime aware learning algorithm to learn a sequence of Bayesian networks (BNs) that model a system that undergoes regime changes. The last BN in the sequence represents the system’s current regime, and should be used for BN inference. To explore the feasibility of the algorithm, we create baseline tests against learning a singe BN, and show that our proposed algorithm outperforms t...