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Jin-Zhu Yü
Jin-Zhu Yü

Jin-Zhu Yü

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Highlighted by Jin-Zhu Yü

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From Bayesian inference problem, MCMC and variational inference by Joseph Rocca

The choice of the family in variational inference sets both the difficulty of the optimisation process and the quality of the final approximation.

From Bayesian inference problem, MCMC and variational inference by Joseph Rocca

Contrarily to sampling approaches, a model is assumed (the parametrised family), implying a bias but also a lower variance. In general VI methods are less accurate that MCMC ones but produce results much faster: these metho…

From Bayesian inference problem, MCMC and variational inference by Joseph Rocca

Contrarily to VI methods described in the next section, MCMC approaches assume no model for the studied probability distribution (the posterior in the Bayesian inference case). As a consequence, these methods have a low bias but a high variance and it implies that results are most of the time more costly to obtain but also more accurate than the one we can get from VI.

Claps from Jin-Zhu Yü

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RL— Introduction to Deep Reinforcement Learning

Jonathan Hui

What is a Positive Definite Matrix?

Aerin Kim 🙏

An intuitive guide to Gaussian processes

Oscar Knagg