What is the difference between random uniform and fixed setting of monte carlo simulation?

jmpneeeewbie

I am making monte carlo simulation with DoE experiment result.

As I understood, monte carlo simulation (simulation experiment of prediction profiler) works with below sequence.

(1) Generate experiments with random X factors with ratin hypercube.

(2) Each experiment has X factors which is generated randomly. Each experiment makes N runs of draws which have X factors as center point and they follow distribution set in prediction profiler.

(3) Calulate failure rate at each experiment.

(Please correct if above things are worng)

 

My question is, what is the difference between random uniform and fixed setting of prediction profiler.

I thought, random normal means the X factors of (2) are following normal distribution durin N runs of draws,

thus, random uniform means the X factors of (2) have uniform distribution for N runs of draws and fixed means the X factors of (2) are fixed during the draw.

 

Please kindly provide your comment what am I understanding correctly.

Victor_G

Hi @jmpneeeewbie,

 

Welcome in the Community !

What you're describing is the generation of points through the Simulator to create a datatable (a subset of the points generated by the Monte Carlo simulations) where you may find informations here : Simulation Experiment (jmp.com)

 

The Monte Carlo simulations is more general than the process you describe.

1. First, you define a domain of possible inputs : you specify the distributions of your input parameters (random normal, uniform, lognormal, Weibull, multivariate, ...). An input parameter set as "fixed" won't have any distribution of values, and only the single point value will be used in the calculations (so you don't take into account any random variability for this input variable for example). See : Simulation Settings for Factors (jmp.com)
2. Then, you can also add noise (random, weighted, multivariate, or no noise) for your responses as well, to take into account variability in the output measurement for example. See : Simulation Settings for Responses (jmp.com)
3. Then, a generation of inputs is done randomly from the inputs probability distributions over the domain you specified in step 1 and 2, when you click on "Simulate" and performs a  deterministic computation on the inputs, thanks to the model you have created before (linking the inputs to output(s)). If you added noise in the outputs, the noises are also taken into consideration in the calculated values.
4. Finally, results are aggregated to create responses distributions, that you can see in the profiler in above "Simulate" button.

 

You can see an example I did with the JMP datatable 2x3x4 Factorial (available in the Sample Data Folder, in "Design of Experiments"). I also join the datatable with the script "Monte Carlo Simulations" if you want to see it in more details.

If you want to find more informations about Monte Carlo simulations, you can find some here : Robust Optimisation of Processes and Products by Using Monte Carlo Simulation Experiments - JMP User Community

 

I hope this answer will help you,

Monte-Carlo_Simulations_2x3x4-Factorial-datatable.jpg

2x3x4 Factorial.jmp