Titlebook: Bayesian Statistical Modeling with Stan, R, and Python; Kentaro Matsuura Book 2022 Springer Nature Singapore Pte Ltd. 2022 Stan.Bayesian M

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Vaccine Development for Cytomegalovirusyze spatial data. It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.

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Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processesyze spatial data. It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.

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Usages of MCMC Samples from Posterior and Predictive Distributionsons and predictive distributions were only kept on very basic levels, such as computing the intervals and visualizations. In this chapter, we will introduce more advanced usages of the MCMC sample. They would be helpful in practice because it is very common to encounter the situation where we need to extract more information from the MCMC sample.

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https://doi.org/10.1007/978-981-19-4755-1Stan; Bayesian Modeling; Statistical Modeling; R; RStan; Python

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