Prior

The pybalonor package supports a prior that is uniform in the mode of logs \(l_0\) and the standard deviation of logs \(l_1\),

\[\phi(l_0, l_1 \,|\, l_0^\text{min}, l_0^\text{max}, l_1^\text{min}, l_1^\text{max}) = \frac{1}{(l_0^\text{max} - l_0^\text{min}) (l_1^\text{max} - l_1^\text{min})}\,,\]

where the rectangular support of \(l_0\) and \(l_1\) is defined by

\[l_0^\text{min}>0\,, \quad l_0^\text{max}>l_0^\text{min},\quad l_1^\text{min}>0\,,\quad \text{and} \quad l_1^\text{max}>l_1^\text{min}\,.\]

The uniform prior weights all parameter combinations \((l_0,l_1)\) within the bounds

\[ \begin{align}\begin{aligned}l_0^\text{min} \leq l_0 \leq l_0^\text{max}\\l_1^\text{min} \leq l_1 \leq l_1^\text{max}\end{aligned}\end{align} \]

equally.

FlatPrior

class FlatPrior(l0_min, l0_max, l1_min, l1_max)

Flat prior of the log-normal distribution.

Parameters

• l0_min (float) – Minimum \(l_0\) within the prior support.

• l0_max (float) – Maximum \(l_0\) within the prior support. Has to fulfill \(\,l_0^\text{max} > l_0^\text{min}\).

• l1_min (float) – Minimum \(l_1\) within the prior support. Has to fulfill \(\,l_1^\text{min} \geq 0\).

• l1_max (float) – Maximum \(l_1\) within the prior support. Has to fulfill \(\,l_1^\text{max} > l_1^\text{min}\).