Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian Networks (Advanced Data Analytics)

Chapmann, Joshua

ISBN 10: 1978304870 / ISBN 13: 9781978304871

Edité par CreateSpace Independent Publishing Platform, 2017

A propos de cet article

USED book in GOOD condition. Great binding, pages and cover show normal signs of wear from use.

N° de réf. du vendeur GCM.2UZF

Détails bibliographiques

• Titre: Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian Networks (Advanced Data Analytics)
• Éditeur: CreateSpace Independent Publishing Platform
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 1978304870
• ISBN à 13 chiffres: 9781978304871
• Reliure: Couverture souple
• État de l'article: good

A propos de ce titre

Synopsis

Markov Models

Présentation de l'éditeur

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