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Sep 5 |
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Probability distribution for a function of a random variable Thats right, its not independent. X's distribution is a function of parameter t (which is known). And t has a known probability distribution. Question remains: what is then distribution of X knowing distribution of t? |
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Sep 5 |
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Probability distribution for a function of a random variable I mean that distribution for parameter t is independent of distribution for X ^^ |
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Aug 19 |
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Plot randomly oriented gaussian kernel I am writting with Python. It does not matter I think. BTW it is multivariate gaussian, so that I have a hint I should add a cross x-y term in exponential. however I would appreciate more precise explanation or pointer to some reference |
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Mar 4 |
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Differentiability for a function made of built-in functions and basic operations How to edit formulas, please? |
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Mar 4 |
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Differentiability for a function made of built-in functions and basic operations Thanks for answer. In first expression for f, x is not in definition domain, so that f is smooth on its open definition domain, ]-infty,0[U]0,infty[ ; in second expression for f, it is smooth on both domains ]-infty,0[ and ]0,infty[ separated by the value where thing under sqrt is zero --> so this verifies the mentionned 'rule' (I am on the interpreter side, who seeks for domains for running optimization routines in which derivatives are needed) |
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Mar 1 |
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Random and Pseudo-random number generation Thanks on comments, I edited above. Regards. |
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Mar 1 |
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Random and Pseudo-random number generation tks a lot, that's kind. |
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Mar 1 |
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Random and Pseudo-random number generation As you prefer. Does this mean that question is migrated or should I migrate it now? tks |
