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  "Title": "Stochastic Newton Sampler (SNS)",
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  "Date": "2022-11-01",
  "Author": "Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A.\nSharabiani",
  "Maintainer": "Alireza Mahani <alireza.s.mahani@gmail.com>",
  "Description": "Stochastic Newton Sampler (SNS) is a\nMetropolis-Hastings-based, Markov Chain Monte Carlo sampler for\ntwice differentiable, log-concave probability density functions\n(PDFs) where the proposal density function is a multivariate\nGaussian resulting from a second-order Taylor-series expansion\nof log-density around the current point. The mean of the\nGaussian proposal is the full Newton-Raphson step from the\ncurrent point. A Boolean flag allows for switching from SNS to\nNewton-Raphson optimization (by choosing the mean of proposal\nfunction as next point). This can be used during burn-in to get\nclose to the mode of the PDF (which is unique due to\nconcavity). For high-dimensional densities, mixing can be\nimproved via 'state space partitioning' strategy, in which SNS\nis applied to disjoint subsets of state space, wrapped in a\nGibbs cycle. Numerical differentiation is available when\nanalytical expressions for gradient and Hessian are not\navailable. Facilities for validation and numerical\ndifferentiation of log-density are provided. Note: Formerly\navailable versions of the MfUSampler can be obtained from the\narchive\n<https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.",
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      "title": "Effective Sample Size Calculator",
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      "title": "Plotting \"sns\" Objects",
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    },
    {
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      "title": "Sample-based prediction using \"sns\" Objects",
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      "title": "Stochastic Newton Sampler (SNS)",
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        "sns"
      ]
    },
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        "sns.check.logdensity"
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      "title": "Utility Functions for Creating and Validating State Space Partitions",
      "topics": [
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        "sns.make.part"
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    },
    {
      "page": "sns.run",
      "title": "Drawing multiple samples using Stochastic Newton Sampler",
      "topics": [
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      ]
    },
    {
      "page": "summary.sns",
      "title": "Summarizing \"sns\" Objects",
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        "summary.sns"
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