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Bayesian inference via approximate data likelihood
Suppose that we have a very large i.i.d. sample $x_1,...,x_n$ and a data likelihood defined by
$$p(x | \theta,\beta) = \prod_ip(x_i | \theta,\beta)$$. Further suppose that $\theta$ is the parameter ...
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