Table of Contents
Notes
Ex
Part A : Combinatorics
Ch 1: Let's count
Parity
Sum & Product
Cell power Ball
Permutations
The subtlety of overcounting
Circular permutation with distinct objects
Circular permutations with indistinguishable objects
Combinations
Balls in cells again
Integer division and Legendre’s formula
Ch 1: Let's count
Ch 2: Inclusion Exclusion
Combinations with repetitions
Derangements
Permutations with forbidden positions
Ch 2: Inclusion Exclusion
Ch 3: Pigeonhole Principle
Ramsey Theorem
Ch 3: Pigeonhole Principle
Ch 4: Recurrence Relations & Generating Functions
Fibonacci and Other Recurrent Sequence
Solving Recurrence Relations
Generating Functions
Ordinary Generating Function
Exponential Generating Function
Ch 4: Recurrence Relations & Generating Functions
Part B : Probability
Ch 1: Introduction
Probability theory (axiomatic approach)
Ch 1: Introduction
Ch 2: Conditional Probability
Bayes' Theorem
Borel-Cantelli lemmas
Ch 2: Conditional Probability
Ch 3: Random Variables I
Probability measure induced by random variable
Widely used probability distributions (discrete)
Widely used probability distributions (continuous)
Cumulative distribution function (cdf)
Mean
Variance
Properties of gamma and beta function*
Summary
Jointly distributed random variables
Independent random variables
Conditional expectation
Conditional variance
Pearson correlation coefficient
Measure theory
Ch 3: Random Variables I
Ch 4: Probability estimates and limit theorems
Markov, Chebyshev, Jensen inequality
Markov's, and Chebyshev inequality
Jensen's inequality
Moment generating function
Existence and uniqueness of mgf
Tail probability estimates using mgf
Probability generating function
Characteristic function
Existence and uniqueness of characteristic function
Sum of random variables
Convolution
Mgf/characteristic function of sum of random variables
Convergence of random variables
Law of large numbers
Central limit theorem
Poisson approximation of binomial
Key takeaways
Ch 4: Probability estimates and limit theorems
Ch 5: Transformation of Random Variables
Ch 5: Transformation of Random Variables
Ch 6: Random Variables II
Ch 6: Random Variables II
Part C : Statistics
Ch 1: Descriptive Statistics
Data Collection
Navigating through biases
Data Representation (Visual)
Histograms
Bar chart
Pie chart
Box plot
Data Representation (Quantitative)
Sample mean
Sample median and percentiles
Sample mode
Sample variance
Skewness
Kurtosis
Pearson correlation coefficient
Ch 1: Descriptive Statistics
Ch 2: Sampling Distributions & Point Estimation
Sample Mean
Mathematical expectation of sample mean
Variance of sample mean
Distribution of sample mean when the population follows $N(\mu, \sigma^{2})$
Sample Variance
Mathematical expectation of sample variance
Distribution of sample variance when the population follows $N(\mu, \sigma^{2})$
Variance of sample variance when the population follows $N(\mu, \sigma^{2})$
Modes of Convergence
Unbiased and Consistent estimator
Maximum Likelihood Estimation (MLE)
Mean Squared Error (MSE)
Bias variance tradeoff
Weak Law of Large Numbers (WLLN)
Central Limit Theorem (CLT)
Key Takeaways
Ch 2: Sampling Distributions & Point Estimation
Ch 3: Interval estimation
Estimation of the population mean
Estimation of population mean when the population variance is known
The $t$-distribution
Estimation of population mean when the population variance is unknown
Estimation of the population variance
The $\chi^{2}$-distribution
The interval estimation
Confidence level vs sample size
Comparison between two population means
Comparison between two population means when the variances are known
Comparison between two population means when the variances are unknown but equal
Comparison between two population variances
The $F$-distribution
The interval estimation
Key Takeaways
Ch 3: Interval estimation
Ch 4: Hypothesis testing
Test of the population mean when population variance is known
Type-II error and power of a test
Test of the population mean when population variance is unknown
Test of population variance
Comparison test between two population means when the variances are known
Comparison test between two population means when the variances are unknown but equal
Comparison test between two population variances
Key Takeaways
Ch 4: Hypothesis testing
Ch 5: Linear Regression
Ch 5: Linear Regression
Part D : Stochastic Processes
Ch 1: Introduction
Ch 1: Introduction
Ch 2: Branching Process
Ch 2: Branching Process
Ch 3: Markov Chain
Ch 3: Markov Chain
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