Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858) - Image 1

£107.98

Springer Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Name: Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)
Brand: Springer
Price: 107.98 GBP
Availability: InStock

AI & Machine Learning

This is the most expensive it has ever been. Walk away.

£108 today · previous high £108 · all-time low £108

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 70 days · 70 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 70 days • 1 price levels

Days at Price

Price Analysis

Most common price: £108 (70 days, 100.0%)

Price range: £108 - £108

Price levels: 1 different prices over 70 days

Description

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers. From the Back Cover This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3030277488
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 19 November 2019
Listed Since: 04 July 2019

Barcode

No barcode data available

Springer Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Introduction to Statistical Modelling and Inference

Current Trends in Bayesian Methodology with Applications

Bayesian Inference: Observations and Applications (Mathematics Research Developments)

Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics)

Bayesian Methods and Ethics in a Clinical Trial Design: 214 (Wiley Series in Probability and Statistics)

Introduction to Bayesian Data Analysis for Cognitive Science (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation: 32 (Chapman & Hall/CRC Biostatistics)

Process Control System Fault Diagnosis: A Bayesian Approach (Wiley Series in Dynamics and Control of Electromechanical Systems)

Probabilistic Machine Learning for Civil Engineers (The MIT Press)

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)

SOME RECENT ADVANCES IN MATHEMATICS AND STATISTICS - PROCEEDINGS OF STATISTICS 2011 CANADA/IMST 2011-FIM XX

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis: 22 (Information Science and Statistics, 22)

The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex Problems

Prior Processes and Their Applications: Nonparametric Bayesian Estimation (Springer Series in Statistics)

Bayesian Optimization in Action

Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition: 68 (Chapman & Hall/CRC Texts in Statistical Science)