A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes
Fundamentals of Statistical Signal Processing-Estimation Fundamentals of Statistical Signal Processing-Estimation Fundamentals of Statistical Signal Processing-Estimation PDF格式
Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free gramm
