Computer-Intensive Statistics: A Promising Interplay between Statistics and Computer Science

Computer Intensive Statistics 1. Introduction

The principle aim of these notes is to provide context and motivation for the APTS Computer Intensive Statistics Module and to make the module as self-contained as is feasible. Statistics is a broad discipline and the APTS cohort naturally has a diverse range of backgrounds. If you have attended the earlier APTS modules this year, especially Statistical Computing and Statistical Inference, then...

Introduction to Statistics for Computer Science Projects

• Usually, there will be both measurement errors and intrinsic variability. For example, we may want to know the run time of an algorithm solving a given problem. The time cannot be measured exactly, but more importantly, the run time will vary because of varying loads on the processor (e.g. from the operating system) and for some languages (e.g. Java) because of the need for periodic garbage c...

Computer-intensive rate estimation, diverging statistics, and scanning

A general rate estimation method is proposed that is based on studying the in-sample evolution of appropriately chosen diverging/converging statistics. The proposed rate estimators are based on simple least squares arguments, and are shown to be accurate in a very general setting without requiring the choice of a tuning parameter. The notion of scanning is introduced with the purpose of extract...

Probability and statistics with reliability, queuing, and computer science applications

Extreme value statistics and traveling fronts: application to computer science.

We study the statistics of height and balanced height in the binary search tree problem in computer science. The search tree problem is first mapped to a fragmentation problem that is then further mapped to a modified directed polymer problem on a Cayley tree. We employ the techniques of traveling fronts to solve the polymer problem and translate back to derive exact asymptotic properties in th...