That sampling method sounds like it should work, assuming it’s all implemented correctly (not sure what method you’re using to sample from the posterior distribution of μ, σ).
Worst case in a million being dominated by parameter uncertainty definitely makes sense, given the small sample size and the rate at which those distributions fall off.
For μ,σ I effectively created a quasi-cumulative distribution with the parameter pairs as the x-axis.
μ1,σ1. μ2,σ1. μ3,σ1 … μ1,σ2. μ2,σ2. μ3,σ2 … μn,σm
The random number defines the relevant point on the y-axis. From there I get the corresponding μ,σ pair from the x-axis.
If this method works I’ll probably have to code the whole thing instead of using a spreadsheet as I don’t have nearly enough μ,σ values to get a good answer currently.
That sampling method sounds like it should work, assuming it’s all implemented correctly (not sure what method you’re using to sample from the posterior distribution of μ, σ).
Worst case in a million being dominated by parameter uncertainty definitely makes sense, given the small sample size and the rate at which those distributions fall off.
For μ,σ I effectively created a quasi-cumulative distribution with the parameter pairs as the x-axis.
μ1,σ1. μ2,σ1. μ3,σ1 … μ1,σ2. μ2,σ2. μ3,σ2 … μn,σm
The random number defines the relevant point on the y-axis. From there I get the corresponding μ,σ pair from the x-axis.
If this method works I’ll probably have to code the whole thing instead of using a spreadsheet as I don’t have nearly enough μ,σ values to get a good answer currently.
At what point is the data used?
I use it to determine the relative probabilities of each μ,σ pair which in turn create the pseudo cdf.
Ok, that sounds right.