New answers tagged random
2
whenever you use Manipulate, use TrackedSymbols to tell it which symbols to track
Manipulate[m = RandomPrime[d];
{m, d}, {d, {20, 50}, SetterBar},
TrackedSymbols :> {d}]
Without this, it will track each symbol in its expression and if any changes, it will re-evaluate again automatically until no more changes are detected.
9
Given your specific example, nothing is "wrong" with ProbabilityDistribution. @MarcoB and @J.M.'stechnicaldifficulties have given the issue: ProbabilityDistributon is more limited with multivariate distributions it knows and your bivariate distribution is not a standard or common distribution.
One can take random samples by finding the marginal ...
1
You can use Catch and Throw:
SeedRandom[0];
Block[{a, b, f, g},
exception = Catch[
Do[
a = RandomReal[{-1, 1}, {2, 2}];
a = a\[Transpose].a;
b = RandomReal[{-1, 1}, {2, 2}];
b = b\[Transpose].b;
f[x_, y_] :=
Det@MatrixFunction[Log, (x + y)/2] - Log[Det[x.y]]/2;
g[x_, y_] := Det[x + y]^(1/2) - Det[x]^(1/2) - Det[y]^(1/2);
...
2
Supposing that you have defined your function f(a,b) and g(a,b), and that a and b are to be 2 by 2 matrices:
Select[Table[a = RandomReal[{-1, 1}, {2, 2}];
b = RandomReal[{-1, 1}, {2, 2}];
If[f[a, b] - g[a, b] < 0, {a, b}], {i, 1000}], #=!=Null &]
This lists all the {a,b} for which the condition holds. You can save a bit (...
3
just in case someone is not interested in real words and wants something faster
StringJoin@RandomChoice[Flatten@{Alphabet[], " "}, 100000]
0
One can exploit the equivalence of evaluating three-term recurrence relations with repeated multiplication of $2\times 2$ matrices for this task. Just like in ubpdqn's solution, I use RandomChoice[] to generate a bunch of $\pm1$ multipliers all at once:
BlockRandom[SeedRandom[42, Method -> "MersenneTwister"]; (* for reproducibility *)
With[{n ...
answered May 16 at 14:44
1
I'm assuming you want to vary the Bernoulli parameter p depending on the pixel grayscale value. Darker areas of the image are more likely to generate a black pixel and lighter areas more likely to be white.
img = Import["ExampleData/ocelot.jpg"];
dat = ImageData[img];
result = Image[
Map[RandomVariate[BernoulliDistribution[#]] &, dat, {2}]]
The ...
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