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  "Title": "GPU-Portable Vendor-Agnostic Metropolis-Hastings Sampler",
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  "Authors@R": "person(\"Pedro\", \"Carvalho Brom\", , \"pcbrom@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-1288-7695\"))",
  "Description": "A generic random-walk Metropolis-Hastings sampler for\nMarkov chain Monte Carlo. The user declares a model by writing\nits log-likelihood and log-prior as one-sided formulas; the\npackage compiles them to a stack-machine bytecode that a single\nportable kernel interprets, so the same model runs on the CPU\nand on the GPU from one source. The sampler advances many\nindependent chains in one batched pass. A distributional\nequivalence harness based on the two-sample Kolmogorov-Smirnov\ntest and the split R-hat statistic of Gelman and Rubin (1992)\n<doi:10.1214/ss/1177011136> checks the GPU output against the\nCPU output. Dispatching the kernel to a GPU helps when many\nchains are run or when the log-density is expensive to evaluate\nover a large data set, and does not help for small models run\nwith few chains.",
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  "Repository": "https://pcbrom.r-universe.dev",
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  "Author": "Pedro Carvalho Brom [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-1288-7695>)",
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      "topics": [
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    },
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      "title": "Run the GPU-portable batched Metropolis sampler",
      "topics": [
        "gpu_metropolis"
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    },
    {
      "page": "gpum_bayes_factor",
      "title": "Bayes factor between two models by thermodynamic integration",
      "topics": [
        "gpum_bayes_factor"
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    },
    {
      "page": "gpum_copula",
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      "topics": [
        "gpum_copula"
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    },
    {
      "page": "gpum_crlb",
      "title": "Observed Fisher information and Cramer-Rao bound for a fit",
      "topics": [
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    },
    {
      "page": "gpum_density_compare",
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      "topics": [
        "gpum_density_compare"
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    },
    {
      "page": "gpum_diagnose",
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      "topics": [
        "gpum_diagnose"
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    },
    {
      "page": "gpum_evidence",
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      "topics": [
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    },
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      "page": "gpum_fit_catalog",
      "title": "Automatic marginal distribution selection for one column",
      "topics": [
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    },
    {
      "page": "gpum_hypothesis",
      "title": "Posterior probability of a hypothesis on one parameter",
      "topics": [
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    },
    {
      "page": "gpum_lfo",
      "title": "Exact leave-future-out cross-validation for dependent data",
      "topics": [
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    },
    {
      "page": "gpum_lm",
      "title": "Declare a conjugate Gaussian linear model",
      "topics": [
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    },
    {
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      "title": "Pareto-smoothed importance-sampling leave-one-out (PSIS-LOO)",
      "topics": [
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      ]
    },
    {
      "page": "gpum_model",
      "title": "Declare a model for the GPU-portable Metropolis sampler",
      "topics": [
        "gpum_model"
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    },
    {
      "page": "gpum_pairs",
      "title": "Pairs plot of the joint posterior",
      "topics": [
        "gpum_pairs"
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    },
    {
      "page": "gpum_ppc",
      "title": "Posterior predictive sample from a fit",
      "topics": [
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    },
    {
      "page": "gpum_region",
      "title": "Credible region of a parameter pair",
      "topics": [
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    {
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      "topics": [
        "gpum_surface"
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    },
    {
      "page": "gpum_ts_model",
      "title": "Declare a Markov (time-series) model by conditional factorisation",
      "topics": [
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    {
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      "topics": [
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    },
    {
      "page": "hdi",
      "title": "Highest posterior density interval",
      "topics": [
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    },
    {
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      "title": "Posterior Kendall's tau of a fitted copula",
      "topics": [
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    {
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      "title": "Batched random-walk Metropolis sampler for a Gaussian mean",
      "topics": [
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      "topics": [
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        "Running the sampler",
        "The fit object",
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        "One source, several backends",
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        "The simple model and its fit",
        "Reading the posterior",
        "Does horsepower add anything? Predictive comparison",
        "Weighing the evidence for horsepower",
        "Observed against the generated distribution",
        "Head-to-head against the established packages",
        "What this case exercised"
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        "The failure mode of plain random-walk Metropolis",
        "Parallel tempering",
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