Quantum Optics: State Reconstruction

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Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers conditional probability distributions and introduces the concept of conditional expected value.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Introduces the basics of plasma physics, covering collective behavior, Debye length, and plasma conditions.