Bayesian Scientific Computing: 215 (Applied Mathematical Sciences, 215) - Image 1

£90.99

Springer Bayesian Scientific Computing: 215 (Applied Mathematical Sciences, 215)

Name: Bayesian Scientific Computing: 215 (Applied Mathematical Sciences, 215)
Brand: Springer
Price: 90.99 GBP
Availability: InStock

Computer Science

It has never been this cheap. We have no record of a lower price.

£91 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 33 days • 33 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 33 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £91 (20 days, 60.6%)

Price range: £91 - £92

Price levels: 2 different prices over 33 days

Description

The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider’s view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3031238230
Category: Books > Subjects > Computing & Internet > Computer Science
Domain: Amazon UK
Release Date: 10 March 2023
Listed Since: 18 November 2022

Barcode

No barcode data available

Springer Bayesian Scientific Computing: 215 (Applied Mathematical Sciences, 215)

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Bayesian Scientific Computing: 215 (Applied Mathematical Sciences, 215)

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Patterns of Scalable Bayesian Inference (Foundations and Trends (R) in Machine Learning)

An Introduction to Statistical Data Science: Theory and Models

Current Trends in Bayesian Methodology with Applications

Inverse Problems: Tikhonov Theory And Algorithms: 22 (Series On Applied Mathematics)

Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)

Bayesian Programming (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Objective Bayesian Inference

Bayesian Inference and Computation in Reliability and Survival Analysis (Emerging Topics in Statistics and Biostatistics)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Bayesian Inference and Computation in Reliability and Survival Analysis (Emerging Topics in Statistics and Biostatistics)

Fundamentals of Nonparametric Bayesian Inference: 44 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44)

Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Bayesian Methods and Ethics in a Clinical Trial Design: 214 (Wiley Series in Probability and Statistics)

An Introduction to Bayesian Analysis: Theory and Methods (Springer Texts in Statistics)

Bayesian Modeling and Computation in Python (Chapman & Hall/CRC Texts in Statistical Science)

Comparative Statistical Inference: 522 (Wiley Series in Probability and Statistics)

Bayesian Nonparametrics (Springer Series in Statistics)

Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support

Bayesian Compendium

The BUGS Book: A Practical Introduction to Bayesian Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)