Name: Maximum Entropy and Bayesian Methods in Applied Statistics: Proceedings of the Fourth Maximum Entropy Workshop University of Calgary, 1984 (en Inglés)
Price: 69500.00 CLP
Availability: InStock
Author: James H. Justice
ISBN: 9780521096034

portada Maximum Entropy and Bayesian Methods in Applied Statistics: Proceedings of the Fourth Maximum Entropy Workshop University of Calgary, 1984 (en Inglés)

Maximum Entropy and Bayesian Methods in Applied Statistics: Proceedings of the Fourth Maximum Entropy Workshop University of Calgary, 1984 (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

James H. Justice

Año

2009

Idioma

Inglés

N° páginas

332

Encuadernación

Tapa Blanda

ISBN

0521096030

ISBN13

9780521096034

N° edición

Categorías

Teoría de la información
Arquitectura de la información

Maximum Entropy and Bayesian Methods in Applied Statistics: Proceedings of the Fourth Maximum Entropy Workshop University of Calgary, 1984 (en Inglés)

James H. Justice (Autor) · Cambridge University Press · Tapa Blanda

Libro Nuevo Importado

Envío: 10 a 14 días háb.

$ 138.990$ 69.500

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 69.500

Llega entre el 07 Ago y el 13 Ago a Santiago, Región Metropolitana. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Maximum Entropy and Bayesian Methods in Applied Statistics: Proceedings of the Fourth Maximum Entropy Workshop University of Calgary, 1984 (en Inglés)"

This collection of papers by leading researchers in their respective fields contains contributions showing the use of the maximum entropy method in many of the fields in which it finds application. In the physical, mathematical and biological sciences it is often necessary to make inferences based on insufficient data. The problem of choosing one among the many possible conclusions or models which are compatible with the data may be resolved in a variety of ways. A particularly appealing method is to choose the solution which maximizes entropy in the sense that the conclusion or model honours the observed data but implies no further assumptions not warranted by the data. The maximum entropy principle has been growing in importance and acceptance in many fields, perhaps most notably statistical physics, astronomy, geophysics, signal processing, image analysis and physical chemistry. The papers included in this volume touch on most of the current areas of research activity and application, and will be of interest to research workers in all fields in which the maximum entropy method may be applied.