Let $\left\lbrace x_1,x_2 \right\rbrace $ be a general bi-variate dependent lognormal distribution, with means $[\mu_1,\mu_2]^T$ and covariance matrix $\Sigma = \begin{pmatrix} \sigma_1^2 & \rho\sigma_1\sigma_2\\ \rho\sigma_1\sigma_2 & \sigma_2^2 \\ \end{pmatrix}$.

The problem is to evaluate a numerical value (analytical solution is not required and probably does not exists) of CDF of the weighted sum of these two variables, $P(w_1x_1+w_2x_2\leqslant\alpha)$, where $w_1$ and $w_2$ are real and non-negative.

Simplifying assumptions, such as $\mu_1=\mu_2$ and/or $\sigma_1=\sigma_2$ may be used, if necessary.

Solution in either Mathematica or Matlab will be appreciated.

For example,

m1 = 0;
m2 = 0;
c={{s1^2,rho s1 s2},{rho s1 s2,s2^2}}
dist = LogMultinormalDistribution[{m1, m2},   c];
PDF[dist, {x, y}]

shows excellent graph.

  • Unfortunately, I do not know how to create the new variable $y=x_1+x_2$ by


without using


first, since it provides two independent distributions.

  • 1
    $\begingroup$ Can you provide some more context on this or any ideas you yourself have? $\endgroup$ Jul 23, 2014 at 8:20
  • 1
    $\begingroup$ Indeed, what have you tried. You do not seriously expect someone code this for you do you? $\endgroup$
    – Lost1
    Jul 23, 2014 at 8:40
  • $\begingroup$ Please take a look at the update above. Sorry for not writing this at the first time - it's my first question on stackexchange ever... $\endgroup$
    – Dima
    Jul 23, 2014 at 9:49

1 Answer 1


Since the sum of lognormal variables do not have closed-form expression, the proposed solution is based on the numerical evaluation of probabilities, as (following example in the question):

NProbability[z < 1, 
z \[Distributed] TransformedDistribution[0.3 x + 0.4 y, 
{x, y} \[Distributed]  LogMultinormalDistribution[{m1, m2}, c]]]

Moreover, the corresponding numerical CDF can be easily evaluated in this way and applied for further analysis.


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