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cs-677sp2010:poisson [2014/12/12 13:32] ryancha created |
cs-677sp2010:poisson [2014/12/12 13:32] (current) ryancha |
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Assume: | Assume: | ||

- | <math> x | \theta \sim Poisson(\theta) </math> | + | $ 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. | ||

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Suppose X is a random variable with the pdf: | Suppose X is a random variable with the pdf: | ||

- | <math> f(x) = 1 </math> | + | $ f(x) = 1 $ |

- | for <math> 0<x<1 </math> and 0 otherwise | + | for $ 0<x<1 $ and 0 otherwise |

- | derive the pdf for <math> Y=-2 ln (x) </math>. | + | 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. |