Andere Kunden interessierten sich auch für
![Enhanced Bayesian Network Models for Spatial Time Series Prediction]( "Enhanced Bayesian Network Models for Spatial Time Series Prediction")
Enhanced Bayesian Network Models for Spatial Time Series Prediction110,99 €![Bayesian Real-Time System Identification]( "Bayesian Real-Time System Identification")
Bayesian Real-Time System Identification117,99 €![Bayesian Real-Time System Identification]( "Bayesian Real-Time System Identification")
Bayesian Real-Time System Identification117,99 €![Introduction to Bayesian Tracking and Particle Filters]( "Introduction to Bayesian Tracking and Particle Filters")
Introduction to Bayesian Tracking and Particle Filters110,99 €![Introduction to Bayesian Tracking and Particle Filters]( "Introduction to Bayesian Tracking and Particle Filters")
Introduction to Bayesian Tracking and Particle Filters110,99 €![Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction]( "Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction")
Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction37,99 €![Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks]( "Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks")
Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks33,99 €
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.