Probability for Actuarial Science
Comprehensive preparation for actuarial probability exams, covering key concepts from set theory to multivariate distributions with practical examples and visual explanations.
Basic Probability Examples
Explore fundamental probability concepts through practical examples, covering conditional probability, independent events, and more.
Role of Mathematics in Data Science and How to Learn - Data Science
Explore the essential mathematical concepts for data science, including statistics, probability, and normal distribution, with practical examples and problem-solving exercises.
Statistics and Data Analysis for Cosmology - Lecture 1
Explore probability concepts in cosmology, including joint and conditional probability, PDFs, independence, Bayes' theorem, and data modeling for analyzing cosmic phenomena.
Total Probability, Bayes' Rule and Tree Diagrams - Probability and Statistics
Explore total probability, Bayes' rule, and tree diagrams in probability and statistics. Learn about mutually independent events and apply these concepts to real-world scenarios, including medical statistics.
Probability Spaces, Events, and Conditional Probabilities
Explore probability spaces, events, and conditional probabilities. Learn about sample spaces, visualizations, independence, and practical applications using chess board examples.
Two-Dimensional Conditional Probability Distribution - Advanced Mathematics
Explore 2D conditional probability distribution with numerical examples, essential for engineering and science students in probability and statistics.
Conditional Probability and Total Probability - Engineering Mathematics
Explore conditional and total probability concepts with practical problems, enhancing your understanding of these crucial engineering mathematics topics.
Probability: Picking Colored Balls from a Box or Bag - Advanced Mathematics
Learn to solve probability problems involving colored balls in boxes or bags, with applications in engineering and advanced mathematics.
Conditional Propagation of Chaos for Generalized Hawkes Processes
Explore conditional propagation of chaos in generalized Hawkes processes with Eva Löcherbach's mathematical analysis of stochastic systems and numerical probability.
Conditional Probability Density Functional Theory - IPAM at UCLA
Explore conditional probability density functional theory for electronic structure calculations, addressing challenges in DFT and offering insights into ground-state correlations and warm dense matter simulations.
Latent Dirichlet Allocation with Gibbs Sampling Explained
Comprehensive explanation of Latent Dirichlet Allocation (LDA) with Gibbs Sampling, covering topic modeling, posterior inference, and implementation details. Ideal for those interested in understanding this classic machine learning technique.
Lilac: A Modal Separation Logic for Conditional Probability
Explore Lilac, a novel separation logic for probabilistic programs, introducing a modality for conditional probability reasoning and validating its effectiveness through formal verification examples.
An AI with an Agenda - How Our Cognitive Biases Leak Into Machine Learning
Explores how human cognitive biases can infiltrate AI and machine learning models, offering strategies to mitigate these issues and create more objective, ethical algorithms.
Probabilistic Model Based on Hausdorff Measures to Represent Unconscious and Conscious Activity of Human Brain
Explore how Hausdorff measures and probabilistic models explain human consciousness, cognitive biases, and selective attention through mathematical frameworks and real-world examples.
Machine Learning in the Real World
Explore real-world challenges in IoT and big data, focusing on data quality, precision, and practical considerations before diving into machine learning algorithms.
Conditional Probability - Lecture 3
Master conditional probability concepts through practical examples and theoretical foundations, enhancing your understanding of statistical dependencies and probabilistic reasoning.
Probability and Statistics: Understanding Independence - Lecture 4
Master the concept of independence in probability theory through detailed explanations, examples, and practical applications in statistical analysis and data science.
Bayes Rule and Its Applications in Machine Learning - Lecture 5
Master Bayes' Rule fundamentals through practical examples and discover its crucial applications in machine learning for calculating model probabilities from data.
Probability and Statistics: Joint Discrete Random Variables and Distributions - Lecture 9
Master probability concepts from exponential distribution to joint discrete random variables, including marginal and conditional probability calculations for comprehensive statistical analysis.
Data-Driven Latent Representations for Time-Dependent Problems - Lecture 3
Explore data-driven latent representations for time-dependent problems, focusing on applications in climate downscaling, superresolution, and optimal transport.