Name: Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) (en Inglés)
Price: 69750.00 CLP
Availability: InStock
Author: Sebastian Reich
ISBN: 9781107663916

portada Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Sebastian Reich

Año

2015

Idioma

Inglés

N° páginas

308

Encuadernación

Tapa Blanda

ISBN13

9781107663916

Categorías

Probabilidad y estadística

Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) (en Inglés)

Sebastian Reich (Autor) · Cambridge University Press · Tapa Blanda

Libro Nuevo

$ 69.750

$ 107.300

Ahorras: $ 37.550

35% descuento

Envío normal

Origen: España (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Martes 23 de Julio y el Martes 30 de Julio.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) (en Inglés)"

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 examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.