例句與造句

1. For many applications, variational Bayes produces solutions of comparable accuracy to Gibbs sampling at greater speed.
2. Both of these expectations are needed when deriving the variational Bayes update equations in a Bayes network involving a Wishart distribution ( which is the conjugate prior of the multivariate normal distribution ).
3. However, when the sample size or the number of parameters is large, full Bayesian simulation can be slow, and people often use approximate methods such as variational Bayes and expectation propagation.
4. In particular, whereas Monte Carlo techniques provide a numerical approximation to the exact posterior using a set of samples, Variational Bayes provides a locally-optimal, exact analytical solution to an approximation of the posterior.
5. The most common type of variational Bayes, known as " mean-field variational Bayes ", uses the Kullback Leibler divergence ( KL-divergence ) of " P " from " Q " as the choice of dissimilarity function.
6. It's difficult to find variational bayes in a sentence. 用variational bayes造句挺難的
7. The most common type of variational Bayes, known as " mean-field variational Bayes ", uses the Kullback Leibler divergence ( KL-divergence ) of " P " from " Q " as the choice of dissimilarity function.
8. In the former purpose ( that of approximating a posterior probability ), variational Bayes is an alternative to Monte Carlo sampling methods  particularly, Markov chain Monte Carlo methods such as Gibbs sampling  for taking a fully Bayesian approach to statistical inference over complex sample from.
9. Variational Bayes can be seen as an extension of the EM ( expectation-maximization ) algorithm from maximum a posteriori estimation ( MAP estimation ) of the single most probable value of each parameter to fully Bayesian estimation which computes ( an approximation to ) the entire posterior distribution of the parameters and latent variables.
10. It can be shown using the calculus of variations ( hence the name " variational Bayes " ) that the " best " distribution q _ j ^ { * } for each of the factors q _ j ( in terms of the distribution minimizing the KL divergence, as described above ) can be expressed as: