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Springer Time Series Analysis State-Space Model with R/Stan

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Description

Explore modern time series analysis with this comprehensive guide from Springer. This book focuses on the state-space model, a subject gaining significant attention across various scientific fields. It provides a consistent Bayesian treatment for both batch and sequential solutions, covering linear Gaussian and general state-space models through MCMC and Kalman/particle filter methods. Readers gain deep insight into flexible modeling techniques. The text offers extensive coverage of state-space models, moving from introductory concepts to advanced applications. By utilizing R and Stan, this resource serves as a concrete illustration for those looking to master modern statistical modeling and time series analysis.

Key Features

This Springer publication provides a concrete illustration of time series analysis centered on the state-space model.

The text offers a consistent Bayesian treatment for combinations of batch and sequential solutions.

Learn to apply MCMC and Kalman/particle filter methods to linear Gaussian and general state-space models.

Gain practical insight into flexible modeling through the use of R and Stan in modern time series analysis.

The content provides extensive coverage of state-space models starting from introductory levels.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
01 September 2022
Listed Since
04 August 2022

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