This appendix provides a summary of the common mathematical notations used throughout this book. Familiarity with these symbols is helpful for understanding the theoretical underpinnings alongside the Python implementations.
Set Theory and Probability Basics¶
| Notation | Meaning | Example | Chapter(s) |
|---|---|---|---|
| , | Sample Space (the set of all possible outcomes) | for a die roll. | 2 |
| Events (subsets of the sample space) | (rolling an even number). | 2 | |
| Empty Set (impossible event) | Rolling a 7 on a standard die. | 2 | |
| Union (‘A or B’ or both occur) | 2 | ||
| Intersection (‘A and B’ both occur) | 2 | ||
| , | Complement (‘not A’) | If , , then . | 2 |
| Set Difference (‘A but not B’) | 2 | ||
| $ | A | $ | Cardinality (number of elements in set A) |
| Probability of event A occurring | for a fair coin. | 2 | |
| $P(A | B)$ | Conditional Probability (prob. of A given B) | $P(\text{Sum}>10 |
Counting Techniques¶
| Notation | Meaning | Example | Chapter(s) |
|---|---|---|---|
| Factorial () | 3 | ||
| , | Permutations (ordered arrangements of k from n) | Ways to award Gold, Silver, Bronze to 3 of 10 runners | 3 |
| , , | Combinations (unordered selections of k from n) | Ways to choose a committee of 3 from 10 people | 3 |
Random Variables and Distributions¶
| Notation | Meaning | Example | Chapter(s) |
|---|---|---|---|
| Random Variables (variables whose values are numerical outcomes) | Number of heads in 3 coin flips. | 6-12 | |
| Specific values (realizations) of random variables | could take the value . | 6-12 | |
| ‘X follows the distribution Dist with given parameters’ | 7, 9 | ||
| , , | Probability Mass Function (PMF) of a discrete RV | for in a Binomial distribution. | 6, 7 |
| , | Probability Density Function (PDF) of a continuous RV | The bell curve shape for a Normal distribution. | 8, 9 |
| , | Cumulative Distribution Function (CDF) | 6, 8 | |
| , , | Expected Value (mean) of RV | Average value expected from many trials. | 6, 8 |
| , , | Variance of RV (measure of spread) | 6, 8 | |
| , , | Standard Deviation of RV () | Spread measured in the same units as . | 6, 8 |
Multiple Random Variables¶
| Notation | Meaning | Chapter(s) |
|---|---|---|
| A pair of random variables | 10-12 | |
| , | Joint PMF of discrete RVs | 10 |
| , | Joint PDF of continuous RVs | 10 |
| , | Joint CDF | 10 |
| , | Marginal PMF/PDF of (derived from joint distribution) | 10 |
| $p(y | x)p_{Y | X}(y |
| $f(y | x)f_{Y | X}(y |
| Covariance between and () | 11 | |
| , | Correlation Coefficient between and () | 11 |
Limit Theorems and Convergence¶
| Notation | Meaning | Chapter(s) |
|---|---|---|
| Convergence in Probability | 13 | |
| Convergence in Distribution | 14 |
Bayesian Inference¶
| Notation | Meaning | Chapter(s) |
|---|---|---|
| Parameter of interest | 5, 15 | |
| Prior distribution of | 15 | |
| $L(\theta | x)$ | Likelihood function |
| $p(\theta | x)$ | Posterior distribution of |
Markov Chains¶
| Notation | Meaning | Chapter(s) |
|---|---|---|
| Transition probability from state to | 16 | |
| Transition Probability Matrix | 16 | |
| Stationary distribution vector | 16 |
General Mathematical Symbols¶
| Notation | Meaning | Chapter(s) |
|---|---|---|
| Summation | Throughout | |
| Integral | Throughout | |
| Approximately equal to | Throughout | |
| Proportional to | 5, 15 | |
| Set of real numbers | Throughout | |
| Set of natural numbers (usually ) | Throughout | |
| ‘Element of’ or ‘belongs to’ | 2 | |
| ‘For all’ | Throughout | |
| ‘There exists’ | Throughout |