Replica Symmetry Breaking: REM

Related lectures (29)

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Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Explains probability distribution, entropy, and Gibbs free entropy, along with the Weiss model.

Explores entropy in information theory and its role in data processing and probability distributions.

Introduces maximum entropy inference for finding optimal probability distributions with minimal extra structure.

Explores computation with tensor networks, covering joint probability distributions, statistical mechanics, and quantum computation applications.

Covers information measures like entropy and Kullback-Leibler divergence.

Explores variational formulation for measuring information content and divergence between probability distributions.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.