when X =
f(x) =
here
here
b)
we can clearly see from f(y| ) that
Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter...
Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter (i) Write down the conditional probability density function of y given μ (ii) Show that rw1p) amp(剖-rr) Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter (i) Write down the conditional probability density function of y given μ (ii) Show that...
Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter (iii) Suppose that we have a prior μ ~ N(a, b-1) where b > 0, Show that the prior distribution π(A) verifies r(11) x exp (iv) Show that the posterior π(μ|y) verifies (v) which distribution is π(μ|y)? Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and...
Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter (vi) Suppose that ( of y with a -ab1. Suppose that you observe a realization Compute the posterior distribution value of 1. π(μ|1) and explain how it relates to π(μ). vii) Suppose now that you observe a second realization of y with a value of -1. Update the posterior π(p11) to incorporate this new information. Bayesian...
Bayesian updating Suppose that we have the model y|μ ~ N(μ, τ-1) where τ > 0 is known and μ is an unknown parameter (vi) Suppose that ( of y with a -ab1. Suppose that you observe a realization Compute the posterior distribution value of 1. π(μ|1) and explain how it relates to π(μ). vii) Suppose now that you observe a second realization of y with a value of -1. Update the posterior π(p11) to incorporate this new information. Bayesian...
11.3) Bayesian Parameter Estimation. Suppose Λ is a random parameter with prior given by the Gamma density 7(a) = CM2-1/4 2 0}, where a is a known positive real number, and I is the Gamma function defined by the integral ['(x) = ( +12'dt, for x > 0 Jo Our observation Yis Poisson with rate A, i.e., p(y) = P({Y = y}|{A =2}) = - - ale-2 - y = 0, 1,2,.... O y! (a) Find the MAP estimate of...
Suppose that X ~ POI(μ), where μ > 0. You will need to use the following fact: when μ is not too close to 0, VR ape x N(VF,1/4). (a) Suppose that we wish to test Ho : μ-710 against Ha : μ μί are given and 10 < μι. m, where 140 and Using 2 (Vx-VHo) as the test statistic, find a critical region (rejection region) with level approximately a (b) Now suppose that we wish to test Ho...
Consider the Bayesian linear regression model with K regressors where v) Now suppose that we have an uninformative prior such that Show that the posterior verifies: N/2 where VĮß-σ2 (XX)-1. Consider the Bayesian linear regression model with K regressors where v) Now suppose that we have an uninformative prior such that Show that the posterior verifies: N/2 where VĮß-σ2 (XX)-1.
Bayesian regression Consider the Bayesian linear regression model with K regressors where (v) Now suppose that we have an uninformative prior such that Show that the posterior verifies 2a2 where VĮß-σ2 (XX)-1. (vi) Now suppose that there is only one regressor li (ie. K = 1). Show that o2 N2 vii) Comment on how the result in part (vi) relates to the choice of prior and standard frequentist (i.e. non-Bayesian) estimators. Bayesian regression Consider the Bayesian linear regression model with...
Bayesian regression Consider the Bayesian linear regression model with K regressors where (v) Now suppose that we have an uninformative prior such that Show that the posterior verifies 2a2 where VĮß-σ2 (XX)-1. (vi) Now suppose that there is only one regressor li (ie. K = 1). Show that o2 N2 vii) Comment on how the result in part (vi) relates to the choice of prior and standard frequentist (i.e. non-Bayesian) estimators. Bayesian regression Consider the Bayesian linear regression model with...
2. Light bulbs are known to have an average lifetime of 2,000 hours. Suppose we model the lifetime of a light bulb by the following probability density function with (yet unknown) parameter c: p(t) = 1-e-t/c when t20 and p(t) = 0 otherwise. (a) Determine the value of the parameter c so that the probability density function has mean 2,000 hours. (b) Determine the probability a lightbulb fails before 1,500 hours. (C) Suppose the lightbulb has already been on for...