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evaluate cdf of posterior distribution of beta at c. m is the prior on beta, a mixture; c is location of evaluation assumption is betahat | beta ~ t_v(beta,sebetahat)
comp_cdf_post(m, c, data)
a k by n matrix
mixture distribution with k components
a scalar
details depend on model
beta = rnorm(100,0,1) betahat= beta+rnorm(100,0,1) sebetahat=rep(1,100) ash.beta = ash(betahat,1,mixcompdist="normal") comp_cdf_post(get_fitted_g(ash.beta),0,data=set_data(beta,sebetahat))
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