We describe the use of a successful combination of Bayesian inference and decision theory in a clinical trial design. The trial involves three important decisions, adaptive dose allocation, optimal stopping of the trial, and the optimal terminal decision upon stopping. For all three decisions we use a formal Bayesian decision-theoretic approach. The application demonstrates how Bayesian posteri...

A b s t r a c t . Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multivalued decision function that predicts a vector of h binary classes. In this paper we obtain the decisio...

This paper introduces a Bayesian Markov regime-switching model that allows the cointegration relationship between two time series to be switched on and off over time. Unlike classical approaches for testing and modeling cointegration, the Bayesian Markov switching method allows for estimation of the regime-specific model parameters via Markov Chain Monte Carlo and generates more reliable estima...

Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian decision trees. In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses or interpretations, and test the model...

Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework t...

Two major approaches have developed within Bayesian statistics to address uncertainty in the prior distribution and in the overall model more generally. First, methods of model checking, including those assessing prior-data conflict, determine whether the prior and the rest of the model are adequate for purposes of inference and estimation or other decision-making. The main drawback of this app...

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theorybased method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost...

Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated with breast cancer survival, the accuracy of survival prognosis models is a challenging issue...

Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of perfor...