Statistics, Bayesian Statistics, Optimization And Ml, Machine Learning, Programming.
Substituting the approximation into the likelihood then gives us We see that the approximated likelihood can be expressed as a function of and it follows that the likelihood is approximately data translated in log r(λ). Thus, we can achieve an approximately noninformative prior if we let η