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Add discrete noise distributions
Adds Poisson, binomial, and negative binomial distributions to the list of noise distributions.
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doc/v2/documentation_data_format.rst

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@@ -748,9 +748,11 @@ Noise distributions
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Denote by :math:`m` the measured value,
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:math:`y:=\text{observableFormula}` the simulated value
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(the location parameter of the noise distribution),
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and :math:`\sigma` the scale parameter of the noise distribution
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as given via the ``noiseFormula`` field (the standard deviation of a normal,
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(for continuous noise distributions,
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the location parameter of the noise distribution),
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and :math:`\sigma` the distribution parameter as given via the ``noiseFormula``
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field (the scale parameter of the continuous noise distributions,
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i.e. the standard deviation of a normal,
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or the scale parameter of a Laplace model).
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Then we have the following effective noise distributions:
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@@ -779,6 +781,28 @@ Then we have the following effective noise distributions:
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- ``log-laplace``
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- .. math::
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\pi(m|y,\sigma) = \frac{1}{2\sigma m}\exp\left(-\frac{|\log m - \log y|}{\sigma}\right)
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* - Poisson distribution
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- ``poisson``
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- .. math::
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\pi(m|\lambda) = \frac{\lambda^m\exp(-\lambda)}{m!}
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where the rate :math:`\lambda` is given via ``observableFormula``.
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``noiseFormula`` must be empty in this case.
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* - Binomial distribution
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- ``binomial``
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- .. math::
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\pi(m|n,p) = \binom{n}{m}p^m(1-p)^{n-m}
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where :math:`n` is the number of trials given via ``observableFormula``
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and :math:`p` the probability of success given via ``noiseFormula``.
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* - Negative binomial distribution
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- ``negative-binomial``
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- .. math::
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\pi(m|r,p) = \binom{m+r-1}{m}p^r(1-p)^m
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where :math:`r` is the number of successes given via ``observableFormula``
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and :math:`p` the probability of success given via ``noiseFormula``.
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The distributions above are for a single data point.
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For a collection :math:`D=\{m_i\}_i` of data points and corresponding

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