Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian Networks: Volume 3 (Advanced Data Analytics) - Tapa blanda

Libro 3 de 3: Advanced Data Analytics

Chapmann, Joshua

 
9781978304871: Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian Networks: Volume 3 (Advanced Data Analytics)
Tapa blanda
ISBN 10: 1978304870 ISBN 13: 9781978304871
Editorial: CreateSpace Independent Publishing Platform, 2017

Sinopsis

Markov Models

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
CreateSpace Independent Publishing Platform
Año de publicación
2017
Idioma
Inglés
ISBN 10 
1978304870
ISBN 13 
9781978304871
Encuadernación
Tapa blanda
Número de páginas
104
Contacto del fabricante
no disponible
Persona responsable
no disponible