Probability and Statistics for Computer Science

James L. Johnson holds a PhD in mathematics from the University of Minnesota and has twenty-five years' experience in academic and industrial computer science. He is currently Professor of Computer Science at Western Washington University. He is also the author of Database: Models, Languages, Design.

Preface. 1. Combinatorics and Probability.

1.1 Combinatorics.

1.2 Summations.

1.3 Probability spaces and random variables.

1.4 Conditional probability.

1.5 Joint distributions.

1.6 Summary.

2. Discrete Distributions.

2.1 The Bernoulli and binomial distributions.

2.2 Power series.

2.3 Geometric and negative binomial forms.

2.4 The Poisson distribution.

2.5 The hypergeometric distribution.

2.6 Summary.

3. Simulation.

3.1 Random number generation.

3.2 Inverse transforms and rejection filters.

3.3 Client-server systems.

3.4 Markov chains.

3.5 Summary.

4. Discrete Decision Theory.

4.1 Decision methods without samples.

4.2 Statistics and their properties.

4.3 Sufficient statistics.

4.4 Hypothesis testing.

4.5 Summary.

5. Real Line-Probability.

5.1 One-dimensional real distributions.

5.2 Joint random variables.

5.3 Differentiable distributions.

5.4 Summary.

6. Continuous Distributions.

6.1 The normal distributions.

6.2 Limit theorems.

6.3 Gamma and beta distributions.

6.4 The X2 and related distributions.

6.5 Computer simulations.

6.6 Summary.

7. Parameter Estimation.

7.1 Bias, consistency, and efficiency.

7.2 Normal inference.

7.3 Sums of squares.

7.4 Analysis of variance.

7.5 Linear regression.

7.6 Summary.

A. Analytical Tools.

B. Statistical Tables.

Bibliography.

Index.