distances Different distances between two given vectors Description These functions are used for calculating different distances between two given vectors. Thus, L1.dist calculates the L1 distance, cosin.dist calculates the cosine distance, euclidian.dist computes the Euclidian distance, and rank.cor.dist computes the rank correlation distance. The vectors have to have same length. When using r...

We consider the well known Least Recently Used (LRU) replacement algorithm and analyze it under the independent reference model and generalized power-law demand. For this extensive family of demand distributions we derive a closed-form expression for the per object steady-state hit ratio. To the best of our knowledge, this is the first analytic derivation of the per object hit ratio of LRU that...

In the past, the creators of numerical programs had to choose between simple expression of mathematical formulas and static type checking. While the Lisp family and its dynamically typed relatives support the straightforward expression via a rich numeric tower, existing statically typed languages force programmers to pollute textbook formulas with explicit coercions or unwieldy notation. In thi...

We study solution methods for boundary value problems associated with the static Kirchhoff rod equations. Using the well known Kirchhoff kinetic analogy between the equations describing the spinning top in a gravity field and spatial rods, the static Kirchhoff rod equations can be fully integrated. We first give an explicit form of a general solution of the static Kirchhoff equations in paramet...

The authors present algorithms to compute the confidence interval for a Poisson parameter (lambda) and the p value for testing the hypothesis that lambda is equal to a constant, which can be used to make inferences about the standardized mortality ratio and the standardized incidence ratio. The p value and confidence interval always agree, despite the discrete nature of the Poisson distribution...

Naive Bayesian classiiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classiication tasks is surprisingly competitive in comparison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the n...

The objective of this paper is the application of two numeric approaches used in the treatment of uncertainty and its respective validation. This research accomplished a study on the probabilistic reasoning in an Expert System: Bayesian Networks and Certainty Factors, presenting as application domain the Clinical Diagnosis of the Epileptic Seizures. The system was developed with KADS methodolog...