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Probabilistic forecasting and Bayesian data assimilation / Sebastian Reich, Universität Potsdam, Germany, Colin Cotter, Imperial College, London.

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In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical exam...

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Bibliographic Details
Online Access: Full Text (via Cambridge)
Main Author: Reich, Sebastian
Other Authors: Cotter, Colin
Format: Electronic eBook
Language:English
Published: Cambridge : Cambridge University Press, 2015.
Subjects:
Bayesian statistical decision theory.
Probabilities.
Uncertainty (Information theory)
Table of Contents:
  • Prologue: how to produce forecasts
  • Part I: Quantifying Uncertainty
  • Introduction to probability
  • Computational statistics
  • Stochastic processes
  • Bayesian inference
  • Part II: Bayesian Data Assimilation
  • Basic data assimilation algorithms
  • McKean approach to data assimilation
  • Data assimilation for spatio-temporal processes
  • Dealing with imperfect models.

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