We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
It is difficult to adopt a simulation technology for simulating a software process because of the difficulty in developing a simulation model. In order to resolve the difficulty, ...
Seunghun Park, KeungSik Choi, Kyung-A Yoon, Doo-Hw...
—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. ...
Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., sco...
— We study the problem of dynamic learning by a social network of agents. Each agent receives a signal about an underlying state and communicates with a subset of agents (his nei...