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I'm attempting to sample from the log normal distribution using numbers.js.

Looking at Wikipedia it looks like I need to solve for mu and sigma. So if I want the mean of the samples to be 10 then I need to solve (Passing in the copied wikipedia markup):

$$10 = {\displaystyle \exp \left(\mu +{\frac {\sigma ^{2}}{2}}\right)}$$

and

$$5 = {\displaystyle [\exp(\sigma ^{2})-1]\exp(2\mu +\sigma ^{2})}$$

I'm I on the right path and is there an online calculator or other simple way (Spreadsheet / Libreoffice) to solve these equations?

Found this in Julia Disourse server:

function myLogNormal(m,std)
    γ = 1+std^2/m^2
    μ = log(m/sqrt(γ))
    σ = sqrt(log(γ))

    return LogNormal(μ,σ)
end

Does it look about right?

Update

Tried out this Javascript version (Generally the numbers look a lot more believable):

    var numbers = require('numbers');
    function params(m,std) {
        let γ = 1+std^2/m^2
        let μ = Math.log(m/Math.sqrt(γ))
        let σ = Math.sqrt(Math.log(γ))
        let n = numbers.random.distribution.logNormal(100, μ, σ);
        console.log(n);;   

        return {mu: μ, sigma: σ};
    }

However when calculating stats from a sampled array the standard deviation looks like it's too large:

    let p = params(10,5);

    console.log(p);
    let n = numbers.random.distribution.logNormal(1000000, p.mu, p.sigma);
    console.log('mathjs std: ', mathjs.std(n));
    console.log('mathjs mean: ', mathjs.mean(n));

LOGGED RESULT:

    { mu: 1.6094379124341003, sigma: 1.1774100225154747 }
    mathjs std:  17.475889552862462
    mathjs mean:  9.972682726284187
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Begin by logging both equations

\begin{align} \mu+\sigma^2/2&=\ln{10}\\ \ln{(e^{\sigma^2}-1)}+2\mu+\sigma^2&=\ln {5} \end{align}

You can substitute line 1 into line 2:

\begin{align} \ln{(e^{\sigma^2}-1)}+2\ln{10}&=\ln{5}\\ \ln{(e^{\sigma^2}-1)}&=\ln {5}-2\ln{10}\\ e^{\sigma^2}&=e^{\ln {5}-2\ln{10}}+1=5/10^2+1\\ \sigma^2&=\ln{\big(5/10^2+1\big)}\\ \sigma&\approx0.2209 \end{align}

So: $\mu=\ln{10}-\sigma^2/2\approx2.278$

In R:

set.seed(1)
m <- 10
v <- 5
(sigma <- sqrt(log(v / m ^ 2 + 1)))
# [1] 0.220885
(mu <- log(m) - sigma ^ 2 / 2)
# [1] 2.27819
mean(x <- rlnorm(5e5, mu, sigma))
# [1] 9.998877
var(x)
# [1] 4.994411
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