Probability and Random Variables
Comprehensive exploration of probability theory and random variables, covering axioms, distributions, expectations, and advanced concepts for a solid foundation in statistical analysis.
Risk and Financial Crises - Probability Theory in Financial Markets
Explore probability theory in financial crises context, covering expected value, variance, covariance, and regression analysis. Learn about risk decomposition and the limitations of normal distribution assumptions in finance.
Probability Bites
Comprehensive video series covering key engineering probability concepts, from basic axioms to advanced topics like hypothesis testing and random number generation.
Statistical Theory
Explore advanced statistical concepts, proofs, and applications using R, covering topics from probability distributions to matrix properties and confidence intervals.
Statistical Methods
Comprehensive lectures on statistical methods covering probability, distributions, hypothesis testing, and regression, with practical examples and review problems.
Probability Basics
Learn fundamental probability concepts, distributions, and statistical techniques essential for data science and analytics.
Reasoning with Data
Explore mathematical concepts like convex hulls, expected value, exponential functions, logarithms, and their applications in real-world scenarios such as polling and profit analysis.
Math for Society
Explore misleading graphs, convex regions, compactness, polls, expected value, compound interest, exponential functions, and logarithms to enhance your mathematical understanding for real-world applications.
Discrete Random Variables
Explore discrete random variables, including binomial, hypergeometric, and Poisson distributions. Learn probability mass functions, expected values, and variance in this comprehensive introduction.
Random Variables
Explore continuous random variables, probability density functions, and the normal distribution. Learn to calculate expected values, variances, and use statistical tables for data analysis.
Excel Statistical Analysis - Build Discrete Probability Distributions with Worksheet Formulas
Learn to create discrete probability distributions using Excel formulas. Master expected value, standard deviation, charting, and applying these concepts to real-world business scenarios for data-driven decision-making.
Statistical Learning IV - Robert Schapire, Microsoft Research
Explore statistical learning with Robert Schapire, covering expected value theorems, ghost samples, PAC learning, and finite hypothesis spaces in this comprehensive lecture.
Discrete Probability Distributions
Comprehensive exploration of discrete probability distributions, covering key concepts, formulas, and applications for various distribution types in statistics and probability theory.
Newcomb's Paradox - Famous Math Problems
Explore Newcomb's paradox, a mathematical-philosophical puzzle involving free will, determinism, and rationality. Learn about expected value and strategies for decision-making in uncertain scenarios.
Random Variables, Means, Variance, and Standard Deviations in Probability and Statistics
Explore key concepts in probability and statistics: random variables, probability distributions, mean, variance, and standard deviation. Learn through examples and practical applications.
Expected Value - Non-uniform Probability Distributions
Explore non-uniform probability distributions and their impact on expected value calculations, enhancing your understanding of statistical concepts.
Expected Value - The $10 Bet (Part 1 of 2)
Explore the concept of expected value through a $10 bet scenario, gaining insights into probability and decision-making in uncertain situations.
Discrete Random Variables - Introduction
Introduction to discrete random variables, exploring their definition, properties, and applications in probability theory and statistics.
Probability and Statistics: An Introductory Overview
Explore probability and statistics fundamentals, from basic concepts to advanced topics, covering applications, history, and key principles in data science and machine learning.
Applicative - The Forgotten Functional Pattern in C++
Explore the powerful applicative pattern in C++, its practical applications, and how it enhances code simplicity and composability across various programming scenarios.
Quantifying Dinosaur Pee - Expressing Probabilities as Floating-Point Values in C++ - Lecture
Explore floating-point precision in C++ through probability calculations, from birthday paradoxes to dinosaur urine, with insights on numerical pitfalls and expected value concepts.