Estimation Models#
The package ships with parametric estimation models covering the most common univariate distributions. Each model can be selected by string name in MMDEstimator (e.g. model="gaussian-loc") or by passing a class instance directly. par_v denotes the variable parameter(s) that are optimised; par_c denotes constant parameter(s) that are held fixed. Click a model name for its full reference page.
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Gaussian \(\mathcal{N}(\mu, \sigma^2)\) with both mean and standard deviation estimated jointly. |
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Gaussian \(\mathcal{N}(\mu, \sigma^2)\) with mean \(\mu\) estimated and standard deviation fixed. |
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Gaussian \(\mathcal{N}(\mu, \sigma^2)\) with standard deviation estimated and mean fixed. |
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Beta \(\mathrm{Beta}(\alpha, \beta)\) with both shape parameters estimated jointly. |
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Beta \(\mathrm{Beta}(\alpha, \beta)\) with shape \(\alpha\) estimated and \(\beta\) fixed. |
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Beta \(\mathrm{Beta}(\alpha, \beta)\) with shape \(\beta\) estimated and \(\alpha\) fixed. |
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Binomial \(B(n, p)\) with success probability \(p\) estimated and number of trials \(n\) fixed. |
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Gamma distribution with both shape and rate estimated jointly. |
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Gamma distribution with shape estimated and rate fixed. |
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Gamma distribution with rate estimated and shape fixed. |
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Poisson distribution with rate \(\lambda\) estimated. |
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Cauchy(loc, 1) — location-only parameterisation matching the R package. |
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1D Dirac mass at |
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Continuous uniform on |
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Continuous uniform on |
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Continuous uniform on |
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Discrete uniform on \(\{1, 2, ..., N\}\) with |
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