Markov Models
Introduction to Markov Chains, Hidden Markov Models and Bayesian networks (Advanced Data Analytics Book 3)
(Kindle Edition)

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What is a MEMORYLESS predictive model?

Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of “memorylessness”, stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored.

This model construction may sound overly simplistic. After all, if you have historical data why not use it to develop more complete and well-informed models? Surely, it would lead to more accurate predictions.

However, when modelling time-series data where previous results are of limited relevance, a memoryless model delivers vast performance advantages. By considering only the present state, algorithms become highly scalable, stable, fast and, above-all-else, extremely versatile. Speech recognition is a perfect example - nearly all of today's speech recognition algorthms are built using Markov Models.

In this book we will explore why a Memoryless predictive model can be so advantageous to the modern tech industry. We will take a look at fundamental mathematics and high-level concepts alike, extending our understanding of the subject beyond the simple Markov Model.

You will learn...

  • Foundations of Markov Models

  • Markov Chains

  • Case Study: Google PageRank

  • Hidden Markov Models

  • Bayesian Networks

  • Inference Tasks

AuthorValerio Pellicciari
BindingKindle Edition
FormatKindle eBook
Language TypePublished
Number Of Pages47
Product GroupeBooks
Publication Date2017-10-13
Release Date2017-10-13
Sales Rank43230

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