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  "Description": "Define distribution families and fit them to\ninterval-censored and interval-truncated data, where the\ntruncation bounds may depend on the individual observation. The\ndefined distributions feature density, probability, sampling\nand fitting methods as well as efficient implementations of the\nlog-density log f(x) and log-probability log P(x0 <= X <= x1)\nfor use in 'TensorFlow' neural networks via the 'tensorflow'\npackage. Allows training parametric neural networks on\ninterval-censored and interval-truncated data with flexible\nparameterization. Applications include Claims Development in\nNon-Life Insurance, e.g. modelling reporting delay\ndistributions from incomplete data, see Bücher, Rosenstock\n(2022) <doi:10.1007/s13385-022-00314-4>.",
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    "trunc_obs",
    "truncate_claims",
    "truncate_obs",
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    "weighted_quantile",
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      "topics": [
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        "blended_transition_inv"
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      ],
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      ]
    },
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      "concept": [
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      ],
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        "dist_beta"
      ]
    },
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      "concept": [
        "Distributions"
      ],
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      ]
    },
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      "concept": [
        "Distributions"
      ],
      "topics": [
        "dist_blended"
      ]
    },
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      "concept": [
        "Distributions"
      ],
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      ]
    },
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      "concept": [
        "Distributions"
      ],
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        "dist_discrete"
      ]
    },
    {
      "page": "dist_empirical",
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      "concept": [
        "Distributions"
      ],
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        "dist_empirical"
      ]
    },
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        "Distributions"
      ],
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        "dist_erlangmix"
      ]
    },
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      "concept": [
        "Distributions"
      ],
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        "dist_exponential"
      ]
    },
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      "concept": [
        "Distributions"
      ],
      "topics": [
        "dist_gamma"
      ]
    },
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      "concept": [
        "Distributions"
      ],
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        "dist_genpareto",
        "dist_genpareto1"
      ]
    },
    {
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      "concept": [
        "Distributions"
      ],
      "topics": [
        "dist_lognormal"
      ]
    },
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      "concept": [
        "Distributions"
      ],
      "topics": [
        "dist_mixture"
      ]
    },
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      "concept": [
        "Distributions"
      ],
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        "dist_negbinomial"
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    },
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        "Distributions"
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        "Distributions"
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        "Distributions"
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      "title": "Base class for Distributions",
      "concept": [
        "Distributions"
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        "Distribution"
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      "concept": [
        "distribution fitting functions"
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        "fit_dist",
        "fit_dist_direct"
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        "fit_dist_start",
        "fit_dist_start.MixtureDistribution"
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        "fit_erlang_mixture"
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      "title": "Fit a generic mixture using an ECME-Algorithm",
      "concept": [
        "distribution fitting functions"
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      "topics": [
        "fit_mixture"
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      "title": "Fit a neural network based distribution model to data",
      "topics": [
        "fit.reservr_keras_model"
      ]
    },
    {
      "page": "flatten_params",
      "title": "Flatten / Inflate parameter lists / vectors",
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        "flatten_bounds",
        "flatten_params",
        "flatten_params_matrix",
        "inflate_params"
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      "title": "The Generalized Pareto Distribution (GPD)",
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        "dgpd",
        "GenPareto",
        "pgpd",
        "qgpd",
        "rgpd"
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        "is.Interval"
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        "interval_intersection",
        "interval_union"
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      "title": "Cast to a TensorFlow matrix",
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      "title": "The Pareto Distribution",
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        "dpareto",
        "Pareto",
        "ppareto",
        "qpareto",
        "rpareto"
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