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seen Apr 7 at 6:20

Apr
7
asked Finding a Gaussian Distribution to approximate a distribution with non-positive definite covariance matrix
Apr
5
comment Test for Validity of Artificial Samples
Rodland I am generating the artificial data sets simply because in real world the actual observed data sets are rare.
Apr
4
awarded  Tumbleweed
Apr
4
comment Test for Validity of Artificial Samples
The statistical structure is a vine copula, so later I use a sampling method for vines to generate sample data.
Apr
4
comment Test for Validity of Artificial Samples
Hi, @Einar Rødland My model is like to construct a model with latent and observed variables(basically to model their correlations) and then I use the observed variables to learn all the necessary parameters.
Apr
4
revised Test for Validity of Artificial Samples
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Apr
4
asked Test for Validity of Artificial Samples
Mar
28
asked Stationary point of Unnormalized and Normalized KL-divergence minimization
Mar
28
comment Gaussian Approximation of an intractable distribution
Thanks for your reply, @Eupraxis1981 the problem with my sample is it is not possible to draw samples nor to implement MCMC due to causality issue(observed variables are the children of hidden variables.)
Mar
25
comment Gaussian Approximation of an intractable distribution
@Eupraxis1981 yes, but is it possible to do that? KL divergence has got an integration form, do you have any examples? Thank you very much!
Mar
25
comment Gaussian Approximation of an intractable distribution
@SeyhmusGüngören it is almost impossible to draw sample from the original distribution so I assume MC method does not work?
Mar
25
revised Gaussian Approximation of an intractable distribution
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Mar
25
comment Gaussian Approximation of an intractable distribution
@SeyhmusGüngören precision matrix is just the inverse of covariance matrix or simply p=c*exp(X_t*K*X) if K is the precision matrix.
Mar
25
asked Gaussian Approximation of an intractable distribution
Sep
8
revised Expectation problem in Absorbing Markov Chain(exercise on Grinstead and Snell 11.2 18 )
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Sep
8
revised Expectation problem in Absorbing Markov Chain(exercise on Grinstead and Snell 11.2 18 )
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Sep
8
revised Expectation problem in Absorbing Markov Chain(exercise on Grinstead and Snell 11.2 18 )
added 57 characters in body
Sep
8
revised Expectation problem in Absorbing Markov Chain(exercise on Grinstead and Snell 11.2 18 )
edited tags
Sep
8
awarded  Editor
Sep
8
revised Expectation problem in Absorbing Markov Chain(exercise on Grinstead and Snell 11.2 18 )
added 128 characters in body