e.g., $\kappa=2$ for uniform and $\kappa=\sqrt{2 \pi} \simeq 2.5$ for normal distribution.

Likelihood becomes zero for large $k_3$, where search for $k_3>1/\sigma$ is pointless anyway due to Criterion 1 (Sect. \[Criterion1\]). The most intriguing application of is when $\sigma$ is still large compared with machine epsilon, and has uncertainty of statistical nature. This allow for selection of maximum likelihood formula(s) in marginal sense, i.e. for $k_3 \sim \sigma^{-1}$. Because calculation of $k_3$ requires storage of all previously computed values, it is reasonable to replace it with $k_2$ using power-law fit from Fig. \[k2k3\]:

$$\label{Id_likelihood3}
\left[  1- \kappa \;  {k_2}^p \;  \sigma\;  P(z) \right] \times  Q(x_i, z, \sigma).$$

Noteworthy, as a function of $k_2$, i.e. number of tested syntactically correct RPN codes, must have a maximum. Formula for $x_i$ with maximum probability is well defined. For limited precision maximum is very pronounced (Fig. \[likelihood\]), but for very small $\sigma$ we might not be able to reach it at all, due to limited computational resources (e.g: Fig. \[likelihood\], solid blue). Equations (\[Id\_likelihood\]-\[Id\_likelihood3\]) can be understood as follows. If we find remarkably simple and elegant formula at the very beginning of the search, which is however quite far from target $z$, e.g., several 