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Introduction to Computation and Programming Using Python_ With Application to Understanding Data (MIT Press) - John V. Guttag
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15 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS
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2022-02-24 01:24:55
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COPYRIGHT
CONTENTS
PREFACE
ACKNOWLEDGMENTS
1 GETTING STARTED
2 INTRODUCTION TO PYTHON
2.1.1 Objects, Expressions, and Numerical Types
2.1.2 Variables and Assignment
2.1.3 Python IDE’s
2.3.1 Input
2.3.2 A Digression About Character Encoding
3 SOME SIMPLE NUMERICAL PROGRAMS
4 FUNCTIONS, SCOPING, AND ABSTRACTION
4.1.1 Function Definitions
4.1.2 Keyword Arguments and Default Values
4.1.3 Scoping
4.3.1 Fibonacci Numbers
4.3.2 Palindromes
5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS
5.1.1 Sequences and Multiple Assignment
5.3.1 Cloning
5.3.2 List Comprehension
6 TESTING AND DEBUGGING
6.1.1 Black-Box Testing
6.1.2 Glass-box Testing
6.1.3 Conducting Tests
6.2.1 Learning to Debug
6.2.2 Designing the Experiment
6.2.3 When the Going Gets Tough
6.2.4 When You Have Found “The” Bug
7 EXCEPTIONS AND ASSERTIONS
8 CLASSES AND OBJECT-ORIENTED PROGRAMMING
8.1.1 Designing Programs Using Abstract Data Types
8.1.2 Using Classes to Keep Track of Students and Faculty
8.2.1 Multiple Levels of Inheritance
8.2.2 The Substitution Principle
8.3.1 Generators
9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
9.3.1 Constant Complexity
9.3.2 Logarithmic Complexity
9.3.3 Linear Complexity
9.3.4 Log-Linear Complexity
9.3.5 Polynomial Complexity
9.3.6 Exponential Complexity
9.3.7 Comparisons of Complexity Classes
10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES
10.1.1 Linear Search and Using Indirection to Access Elements
10.1.2 Binary Search and Exploiting Assumptions
10.2.1 Merge Sort
10.2.2 Exploiting Functions as Parameters
10.2.3 Sorting in Python
11 PLOTTING AND MORE ABOUT CLASSES
12 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS
12.1.1 Greedy Algorithms
12.1.2 An Optimal Solution to the 0/1 Knapsack Problem
12.2.1 Some Classic Graph-Theoretic Problems
12.2.2 Shortest Path: Depth-First Search and Breadth-First Search
13 DYNAMIC PROGRAMMING
14 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION
15 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS
15.4.1 Probability Distributions
15.4.2 Normal Distributions
15.4.3 Continuous and Discrete Uniform Distributions
15.4.4 Binomial and Multinomial Distributions
15.4.5 Exponential and Geometric Distributions
15.4.6 Benford’s Distribution
16 MONTE CARLO SIMULATION
17 SAMPLING AND CONFIDENCE INTERVALS
18 UNDERSTANDING EXPERIMENTAL DATA
18.1.1 Using Linear Regression to Find a Fit
18.2.1 Coefficient of Determination
18.2.2 Using a Computational Model
19 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING
20 CONDITIONAL PROBABILITY AND BAYESIAN STATISTICS
21 LIES, DAMNED LIES, AND STATISTICS
22 A QUICK LOOK AT MACHINE LEARNING
23 CLUSTERING
24 CLASSIFICATION METHODS
PYTHON 3.5 QUICK REFERENCE
INDEX
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