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Conditional Probability: Understanding Events and Their Relationships
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Related lectures (51)
Conditional Probability: Definition and Examples
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Explains the calculation of conditional probability with definitions and examples.
Probability Fundamentals
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Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.
Normal Distribution: Properties and Calculations
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Covers the normal distribution, including its properties and calculations.
Bayes' Theorem: Applications and Simulations
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Covers the application of Bayes' Theorem in practical reasoning, especially in clinical settings.
Elements of Statistics: Probability and Random Variables
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Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.
Law of Large Numbers: Strong Convergence
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Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
Expectation Maximization: Learning Parameters
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Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.
Prediction Decomposition: Probability and Permutations
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Discusses prediction decomposition in probability theory and explores random permutations.
Applications of Quantum Science: Densities and Statistics
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Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.
Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE
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Explores memorylessness in distributions, stationary processes, and estimation using MLE.
Central Limit Theorem
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Covers the central limit theorem, showing how random processes converge to a normal distribution.
Statistical Theory: Maximum Likelihood Estimation
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Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
Gaussian Mixture Models & Noisy Signals
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Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.
Probability Fundamentals
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Covers the basic concepts of probability, including sample space, events, intersections, and independence.
Martingales and Brownian Motion: Leaving Intervals and Maximum Distribution
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Explores the average time for a Brownian motion to leave an interval and the maximum distribution.
Statistical Analysis: Hypothesis Testing and Distribution Models
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Covers statistical analysis techniques, hypothesis testing, and distribution models using R software.
Understanding Risk and Uncertainty
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Explores risk, uncertainty, subjective probabilities, and decision-making under different scenarios.
Stochastic Models for Communications
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Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.
Equidistribution of CM Points
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Explores the joint equidistribution of CM points and their properties in ergodic theory and homogeneous dynamics.
Binomial and Poisson Mass Functions
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Explores binomial and Poisson mass functions, calculating probabilities and discussing distribution functions of random variables.
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