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I'm sampling a DirichletDistribution like so:

n = 5000;
par1 = Table[1./n,n];
data1 = RandomVariate[DirichletDistribution[par1],50];  
(* This takes about 1.5 seconds *)
par2 = Table[1.,n];
data2 = RandomVariate[DirichletDistribution[par2],50];  
(* This takes about 0.10 seconds *)

If I play with the value of the parameters, I find that giving values > 1 radically improves the performance. Any clues on how to circumvent this?

I'm sampling a DirichletDistribution like so:

n = 5000;
par1 = Table[1./n,n];
data1 = RandomVariate[DirichletDistribution[par1],50];  
(* This takes about 1.5 seconds *)
par2 = Table[1.,n];
data2 = RandomVariate[DirichletDistribution[par2],50];  
(* This takes about 0.10 seconds *)

If I play with the value of the parameters, I find that giving values > 1 radically improves the performance. Any clues on how to circumvent this?

EDIT : The reason why I'm comparing par1 and par2 is that I was hoping for a shortcut following this kind of reasoning (although I know that this is false): DirichletDistribution[n*par] / n

I'm sampling a DirichletDistribution like so:

n = 5 000;

par1 = Table[1./n,n];

data1 = RandomVariate[DirichletDistribution[par1],50]; (* This takes about 1.5 seconds *)

par2 = Table[1.,n];

data2 = RandomVariate[DirichletDistribution[par2],50]; (* This takes about 0.10 seconds *)

If I play with the value of the parameters, I find that giving values > 1 radically improves the performance.

  Any clues on how to circumvent this? Thanks!

I'm sampling a DirichletDistribution like so:

n = 5000;
par1 = Table[1./n,n];
data1 = RandomVariate[DirichletDistribution[par1],50];  
(* This takes about 1.5 seconds *)
par2 = Table[1.,n];
data2 = RandomVariate[DirichletDistribution[par2],50];  
(* This takes about 0.10 seconds *)

If I play with the value of the parameters, I find that giving values > 1 radically improves the performance. Any clues on how to circumvent this?