I have 1000 histograms obtained from a Monte-Carlo complex simulation process. So to explain what I am searching I will simplify my problem.

If I want to draw 3 histograms on the same scale, I will write a program like the following one

h1 = RandomVariate[NormalDistribution[], 1000];
h2 = RandomVariate[NormalDistribution[0, .2], 1000];
h3 = RandomVariate[NormalDistribution[0, .3], 1000];
Histogram[{h1, h2, h3}]

which will gives

enter image description here

But, this graphic is not easy to read and there is a third temporal axis, since all distributions are obtained step after step. So I would add a third axis and draw the histogram obliquely with the 0 of the two others axes situated on the third one, as is the usage for stochastic processes.

Thanks for the help

  • $\begingroup$ If you have lots of "data" (which by definition one gets with Monte Carlo simulations), you want to abandon the use of histograms and use nonparametric density estimates which can be constructed in Mathematica using SmoothKernelDistribution. This approach is especially better than histograms when you want to show multiple curves. And you can produce the third axis easily. 3D "smoothed histograms" are also available. $\endgroup$
    – JimB
    May 31, 2017 at 15:08

2 Answers 2


To emphasize my comment: when one wants to display many histograms, one really needs to use nonparametric density estimators. Here's an example:

nSimulations = 25;
nSamples = 1000;  (* Sample size for each pdf *)
n = 1000;  (* Number of points for drawing each pdf *)
pdfs = ConstantArray[0, {nSimulations, n}];
μ = Table[-1. + 4 (i - 1)/(nSimulations - 1), {i, nSimulations}];
σ = Table[1. - 0.8 (i - 1)/(nSimulations - 1), {i, nSimulations}];
Do[h = SmoothKernelDistribution[RandomVariate[NormalDistribution[μ[[i]], σ[[i]]], nSamples]];
 pdfs[[i]] = Table[{i, x, PDF[h, x]}, {x, -5, 5, 10/n}],
 {i, nSimulations}]

ListPointPlot3D[pdfs, PlotRange -> All, PlotStyle -> Black]

Multiple smooth kernel distributions in 3D


One approach is to use Histogram3D:

Histogram3D[{Transpose@{ConstantArray[1, Length[h1]], h1}, 
  Transpose@{ConstantArray[2, Length[h2]], h2}, 
  Transpose@{ConstantArray[3, Length[h3]], h3}}]

enter image description here

More generally, if you place all your data in a big matrix:

h = {h1, h2, h3}; 
Histogram3D[Transpose[{ConstantArray[#, Length[h[[#]]]], h[[#]]}] & /@ Range[3]]

then you can plot all 1000 data sets just by changing the parameter in the Range function.


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