Quantum Sensing: Parameter Estimation and Bounds

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Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Covers methods to estimate probability distributions from samples.

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Explores environmental legislation, noise protection, and exposure limits for road traffic and aircraft noise.

Covers decoding methods and training challenges in natural language generation.

Introduces Fisher Information for parameter estimation based on collected data.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores the wave-particle dualism in quantum mechanics and the quantification of energy levels in atoms.