What do you think?
Rate this book
Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian networks
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 Google PageRank Hidden Markov Models Bayesian Networks Inference Tasks
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 Google PageRank Hidden Markov Models Bayesian Networks Inference Tasks
106 pages, Kindle Edition
Published October 13, 2017
43 people are currently reading
14 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 2 of 2 reviews
February 11, 2018
Too terse.
October 10, 2018
Horrible editing
They clearly didn't hire a copy editor on this. I'd be surprised if someone read this twice. Do not buy this book!
They clearly didn't hire a copy editor on this. I'd be surprised if someone read this twice. Do not buy this book!
Displaying 1 - 2 of 2 reviews