Dr. Hari M. Koduvely  | Packt Publishing

19,000원  | 20160701  | 9781783987603

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources.

Gregoriou, Greg N./Pascalau, Razvan/  | Palgrave Macmillan

101,040원  | 20210101  | 9780230283657

This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.

Hobbs, N. Thompson, Hooten, Mevin B.  | Princeton University Press

99,220원  | 20150804  | 9780691159287

What if a teacher could design a lesson that he knew students would remember twenty years later? We all have defining moments in our lives - meaningful experiences that stand out in our memory.

 | Springer Nature B.V.

73,480원  | 20101221  | 9781349328956

This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.

Hilbe, Joseph M., De Souza, Rafael S., Ishida, Emille E. O.  | Cambridge Univ Pr

176,400원  | 20170531  | 9781107133082

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly.

 |

0원  | 20250523  | 9781466513600

Bayesian nonparametric methods, in particular nonparametric mixture models, have become extremely popular in the last 10 years and are becoming part of the foundational material that any Bayesian statistician needs to be familiar with. This book introduces Bayesian nonparametric mixture models to readers with intermediate knowledge of Bayesian statistical methods and computation using simulation-based methods such as Markov chain Monte Carlo. Suitable for professional statisticians and graduate students, it is one of the first books to offer an introductory text on Bayesian nonparametric mixture modeling.

 | Taylor & Francis Ltd

115,800원  | 20210930  | 9781032177151

This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.

 | KS OmniScriptum Publishing

180,070원  | 20100519  | 9783838356556

Addressing salinity management problem, at catchment scales requires an integrated modelling approach, in which key bio-physical and socio-economic drivers, processes and impacts are all considered. The problems are also characterised by data and knowledge that is sparse, scattered and of varying type and quality. Therefore, the approach must have the ability to use and integrate both qualitative and quantitative data and information extracted from a range of pre-existing sources.

 | Springer Nature B.V.

73,480원  | 20211118  | 9783030761257

Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering.

Peter D. Congdon  | Chapman & Hall

278,430원  | 20191003  | 9781498785754

In the tradition of The Perks of Being a Wallflower comes a story about choosing your path and finding the courage to be yourself in the face of fear.

Giakoumatos, Stefanos  | LAP Lambert Academic Publishing

183,750원  | 20141201  | 9783838386331

The phenomenon of changing variance and covariance is often encountered in financial time series. As a result, during the last years researchers focused on the time-varying volatility models. These models are able to describe the main characteristics of the financial data such as the volatility clustering.

West, Mike  | Springer

202,100원  | 20151231  | 9781475770988

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

 | Springer Nature B.V.

73,480원  | 20130317  | 9781475770971

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques.

Barber, David, Cemgil, A. Taylan, Chiappa, Silvia  | Cambridge University Press

271,950원  | 20111203  | 9780521196765

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.

Ruggeri, Fabrizio  | John Wiley & Sons Inc

195,790원  | 20120709  | 9780470744536

Bayesian Analysis of Stochastic Process Models provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making, and important applied models based on stochastic processes.