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 cs-677sp2010:poisson [2014/12/12 13:32]ryancha created cs-677sp2010:poisson [2014/12/12 13:32] (current)ryancha 2014/12/12 13:32 ryancha 2014/12/12 13:32 ryancha created 2014/12/12 13:32 ryancha 2014/12/12 13:32 ryancha created Line 1: Line 1: Assume: Assume: - <​math> ​x | \theta \sim Poisson(\theta) ​​ + $x | \theta \sim Poisson(\theta) ​$ # What distribution would make a good (that is, derive the conjugate) prior? Show how you came to this conclusion, that is, do not just look it up. # What distribution would make a good (that is, derive the conjugate) prior? Show how you came to this conclusion, that is, do not just look it up. Line 12: Line 12: Suppose X is a random variable with the pdf: Suppose X is a random variable with the pdf: - <​math> ​f(x) = 1 ​ + $f(x) = 1$ - for <​math> ​0<​x<​1 ​ ​and 0 otherwise + for $0<​x<​1 ​$ and 0 otherwise - derive the pdf for <​math> ​Y=-2 ln (x) ​. + derive the pdf for $Y=-2 ln (x)$. == One more Change of Variables == == One more Change of Variables == Oh, never mind, maybe next year. Oh, never mind, maybe next year.