A01=Joao Paulo Coelho
A01=Jose Boaventura-Cunha
A01=Tatiana M. Pinho
advanced Markov model implementation
Age Group_Uncategorized
Age Group_Uncategorized
AR Model
ARMM
Author_Joao Paulo Coelho
Author_Jose Boaventura-Cunha
Author_Tatiana M. Pinho
automatic-update
Autoregressive Model Coefficients
Backward Probabilities
Basis Density Functions
Baum-Welch training algorithm
Cardiotocography Interpretation
Category1=Non-Fiction
Category=PBT
Continuous Hidden Markov Model
COP=United Kingdom
Delivery_Delivery within 10-20 working days
Discrete Hidden Markov Model
empirical data modeling
eq_isMigrated=2
eq_nobargain
Fetal Heart Rate
Gaussian Functions
Gaussian Mixture
Gaussian Probability Distribution Function
Hidden Markov Model
hidden Markov models
Hidden State
Language_English
Markov chain analysis
mathematical signal processing
Matlab
Model dynamics
Multivariate Gaussian Functions
PA=Available
Price_€100 and above
probabilistic algorithms
Probability Density Function
Probability transitions estimation
PS=Active
RBF Neural Network
Sample Space
softlaunch
speech processing
stochastic modeling
Stochastic processes
Stock Prices Prediction
Takagi Sugeno Fuzzy Model
time series forecasting
Time Series Model Structure
Time Series Prediction
Variable P3
Viterbi Algorithm
Yule Walker Method
Product details
- ISBN 9780367203498
- Weight: 748g
- Dimensions: 156 x 234mm
- Publication Date: 13 Aug 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.
Key Selling Points:
- Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory
- Covers the analysis of both continuous and discrete Markov chains
- Discusses the translation of HMM concepts from the realm of formal mathematics into computer code
- Offers many examples to supplement mathematical notation when explaining new concepts
João Paulo Coelho is an adjunct professor, and currently the Electrical Engineering course director, at the Polytechnic Institute of Bragança. He is also a researcher at CeDRI and holds a Ph.D. degree in computational intelligence applied to agricultural greenhouses. He has been involved, as a researcher member, in several scientific projects at both the national and European level. His research interests include control systems design, machine learning, electronic instrumentation, embedded systems and discrete-event computer simulation.
Tatiana M. Pinho graduated in Energy Engineering from the University of Trás-os-Montes e Alto Douro (UTAD), Portugal in 2011 and received the MSc degree in Energy Engineering from UTAD in 2013. In 2018, she received the Ph.D. degree in Electrical and Computer Engineering in UTAD and INESC TEC Technology and Science, supported by the FCT. Presently she is a postdoctoral researcher at the INESC TEC and her research interests include systems’ modeling and adaptive control.
José Boaventura-Cunha graduated in Electronics and Telecommunications Engineering and has a Ph.D. degree in Electrical and Computer Engineering. Presently he is an Associate Professor with habilitation at the UTAD University, a senior researcher at the INESC-TEC and member of IFAC and IEEE. He has coordinated/participated in several national and international research projects aiming the development of new instrumentation, modelling and control technologies applied to agriculture. His research interests include modeling, system identification and adaptive control.
