For a family of distribution \, \{ \textsf{Ber}(p)\} _{p\in (0,1)}

Consider the posterior distribution derived in the worked example from the previous lecture.To recap, our parameter of interest
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Consider the case where we still have observations X_1, X_2, \cdots X_ n, \stackrel{\text {i.i.d}}{\sim } \textsf{Ber}(p).
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Now, consider the case where our prior distribution is still \textsf{Beta}(a, b). Suppose we are to change our conditional
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For a family of distribution \, \{ \textsf{Ber}(p)\} _{p\in (0,1)} \, , Jeffreys prior is proportional to:\, \pi _ j(p) \propto
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A circle has a radius of 7centimeters (cm). What is the approximate area of the circle? Use the approximation of 227 for π in
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Which measurement is a good estimate for the area of a circle if the radius is 4meters? Responses 12.6 m2 12 point 6 textsf m
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For a family of distribution [mathjaxinline]\, \{ \textsf{Poiss}(\lambda )\} _{\lambda >0} \,[/mathjaxinline] , Jeffreys prior
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Let us think about what goes wrong when we drop the assumption that \textsf{Var}(X) \neq 0 in theoretical linear regression.Let
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Use the value you found for Latex: f(x)f ( x ) to find Latex: f^{-1}\left(f(x)\right)\textsf{.} For example, if Latex:
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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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