The rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Naïve Bayesian model is applied in process of classification. A small set of high ...

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learnin...

1 I n t r o d u c t i o n The Dempster-Shafer theory is quite popular in knowledge based applications. However, it's exponential computational complexity is a stumbling block. Several researchers worked on the problem of reducing the computational burden of the theory. The work in this direction was initiated by Barnett [1]. The approach of reducing the number of focal elements by certain appro...

Human estimates of the probabilities of combinations of events show well-established violations of probability theory, most notably the conjunction and disjunction fallacies. These violations have led researchers to conclude that the rules of probability are too complex for most people to use, and that cognitively-easier approximations such as averaging are used instead. Unlike previous work th...

This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective. In statistical inference based on the Bayes’ rule it is assumed that some prior knowledge (prior probability) about some parameters without knowledge about the data is given first. Next the posterior probability is computed by employing the available data. The posterior probabilit...

The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical Bayesian approach, in which the system learns a prior distribution over rule sets. We present a class of prior distributions parameterized by a r...

Group sequential stopping rules are often used as guidelines in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g. estimates of treatment effect) and statistical (e.g. frequentist...

We consider the problem of gambling on a quantum experiment and enforce rational behaviour by a few rules. These rules yield, in the classical case, the Bayesian theory of probability via duality theorems. In our quantum setting, they yield the Bayesian theory generalised to the space of Hermitian matrices. This very theory is quantum mechanics: in fact, we derive all its four postulates from t...

We formulate and evaluate a Bayesian approach to probabilistic input modeling. Taking into account the parameter and stochastic uncertainties inherent in most simulations, this approach yields valid predictive inferences about the output quantities of interest. We use prior information to construct prior distributions on the input-model parameters. Combining this prior information with the like...

Bayesian models of cognition are typically used to describe human learning and inference at the computational level, identifying which hypotheses people should select to explain observed data given a particular set of inductive biases. However, such an analysis can be consistent with human behavior even if people are not actually carrying out exact Bayesian inference. We analyze a simple algori...