Predictions and Bounds of IDA* with Conditional Distributions

Predicting the Performance of IDA* using Conditional Distributions

Korf, Reid, and Edelkamp introduced a formula to predict the number of nodes IDA* will expand on a single iteration for a given consistent heuristic, and experimentally demonstrated that it could make very accurate predictions. In this paper we show that, in addition to requiring the heuristic to be consistent, their formula’s predictions are accurate only at levels of the brute-force search tr...

Stochastic bounds for a single server queue with general retrial times

We propose to use a mathematical method based on stochastic comparisons of Markov chains in order to derive performance indice bounds‎. ‎The main goal of this paper is to investigate various monotonicity properties of a single server retrial queue with first-come-first-served (FCFS) orbit and general retrial times using the stochastic ordering techniques‎.

Modeling Conditional Distributions of Continuous Variables in Bayesian Networks

The MTE (mixture of truncated exponentials) model was introduced as a general solution to the problem of specifying conditional distributions for continuous variables in Bayesian networks, especially as an alternative to discretization. In this paper we compare the behavior of two different approaches for constructing conditional MTE models in an example taken from Finance, which is a domain we...

Predicting the Performance of IDA* with Conditional Distributions

(Korf, Reid, and Edelkamp 2001) introduced a formula to predict the number of nodes IDA* will expand given the static distribution of heuristic values. Their formula proved to be very accurate but it is only accurate under the following limitations: (1) the heuristic must be consistent; (2) the prediction is for a large random sample of start states (or for large thresholds). In this paper we g...

Some New Characterization Results on Exponential and Related Distributions