detection and estimation theory: lecture notes

#1, Introduction to Detection and Estimation Theory

#2, Probability Review

#3, Estimator Performance

#4, Exponential Family of Distributions

#7, Efficient Estimation of Scalar Parameters

#8, Multiparameter and Gaussian Cramér-Rao Bounds

#9, Linear Model

#10, ML Estimation

#11, Multinomial Measurements and ML Estimation of Class Probabilities

#12, EM Algorithm

#2, Bayesian Estimation

#3, Sequential Bayesian Approach and Prediction

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#4, Bayesian Inference for Gaussian Linear Model

#5, Noninformative Priors

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#6, Bayesian Inference for Multiparameter Models

#7, EM Algorithm for Marginal MAP Estimation

#1, Introduction to Detection Theory

#2, Bayesian Detection

#3, Bayesian Detection Examples

#4, Chernoff Bound on Average Error Probability

#5, Bayesian Classification

#6, Introduction to Frequentist Detection, Simple Hypotheses

#7, Frequentist Detection of Composite Hypotheses

#2, Kalman filter

#3, Hidden Markov Models