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  "Title": "Efficient Implementation of Gaussian Process in Bayesian\nHierarchical Models",
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  "Description": "Implements Bayesian hierarchical models with flexible\nGaussian process priors, focusing on Extended Latent Gaussian\nModels and incorporating various Gaussian process priors for\nBayesian smoothing. Computations leverage finite element\napproximations and adaptive quadrature for efficient inference.\nMethods are detailed in Zhang, Stringer, Brown, and Stafford\n(2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown,\nand Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang,\nBrown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and\nStringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.",
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      ]
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    {
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      "title": "Computing the posterior samples of the function or its derivative using the posterior samples of the basis coefficients for iwp",
      "topics": [
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    {
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      "title": "Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGP",
      "topics": [
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    },
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      "title": "Constructing the precision matrix given the knot sequence",
      "topics": [
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      "topics": [
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      "title": "Construct posterior inference given samples",
      "topics": [
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