\, \lambda \sim \textsf{Exp}(\alpha )

\, \lambda \sim \textsf{Exp}(\alpha ) \, for some \, \alpha >0 \, and conditional on \, \lambda \,, \, X_1,\ldots ,X_
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Now, suppose that we instead have the proper prior \pi (\lambda ) \sim \textsf{Exp}(a) (a > 0). Again, just as in part (b):
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The prior \lambda is distributed according to \textsf{Exp}(a) (a > 0). Write the probability distribution function \pi (\lambda
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Suppose we have the improper prior \pi (\lambda ) \propto e^{-a\lambda }, \lambda \in \mathbb {R} (and a \geq 0). Conditional on
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Consider the posterior distribution derived in the worked example from the previous lecture.To recap, our parameter of interest
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For a family of distribution [mathjaxinline]\, \{ \textsf{Poiss}(\lambda )\} _{\lambda >0} \,[/mathjaxinline] , Jeffreys prior
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We are given the additional information that conditional on the parameter of interest \lambda, our observations X_1, X_2,
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Prove: let alpha be a lower bound of a subset E of an ordered set S. If alpha exist in E, then alpha= infE(greatest lower bound
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2. asked by Ashley
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Suppose we have the prior [mathjaxinline]\pi (\lambda ) \sim[/mathjaxinline] [mathjaxinline]\textsf{Gamma}(a, b)[/mathjaxinline]
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The lifetime (in thousands of hours) X of a light bulb has pdfg(x)= \lambda e^{-\lambda x}, \hspace{3mm} x\geq 0 for some
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2. 122 views