Linear regression serves as a fundamental stepping stone into the world of machine learning, embodying both simplicity and the power of predictive analytics. Conceptually, it rests on a graceful mathematical framework that elegantly unravels its potential and delineates its limitations. This guide will walk you through the mathematical fundamentals, offering a clear exposition of its foundational … Continue reading Advanced – Linear Regression
(Part One: AWGN Model) A useful example of MAP estimation was NASA's 1997 US patent, pertaining to the invention of a MAP decoder for digital communications. MAP decoding is a probabilistic decoding method that selects the most likely transmitted sequence given the received sequence and the channel's statistical properties. It is fundamental in the field … Continue reading Advanced – Maximum A Posteriori Estimation Decoding
In statistical inference, one often encounters a dataset $latex X = \{x_1, x_2, \ldots, x_k\} \subset \mathbb{R}^n$ and seek to characterize it by estimating the parameters $latex \theta$ of a chosen probability distribution $latex p(X | \theta)$. A prevalent technique for achieving this is Maximum Likelihood Estimation (MLE). At its core, MLE is the method … Continue reading Advanced – Maximum Likelihood Estimation
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