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You are here		www.nowozin.net Becoming a Bayesian, Part 2 - Sebastian Nowozins slow blog
\|	\|	djalil.chafai.net How to speed up MCMC algorithms - Libres pensées d'un mathématicien ordinaire	4.2 parsecs away Travel
\|	\|	Markov-Chains-Monte-Carlo (MCMC for short) methods are widely used in practice for the approximate computation of integrals on various types of spaces. More precisely, let \(\mu\) be a probability measure on \(E\), known only up to a multiplicative constant. Let \(K\) be an irreducible Markov kernel on \(E\). Then by using a classical Metropolis-Hastings type construction, one cook up a computable...	4.2 parsecs away Travel
\|	\|	jmlr.org Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting	3.8 parsecs away Travel
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\|	\|	www.yulingyao.com Bayes is guaranteed to overfit, for any model, any prior, and every data point - Yuling Yao's Blog	4.5 parsecs away Travel
\|	\|	The myth A few years ago I saw a StackOverflow question (but I cannot find it now): allegedly Andrew Gelman blogged that Bayesian models would not overfit and would only underfit (I also cannot find Andrew's blog post now but I think what he meant by "Bayesian models only underfits" is that you can never fully model the true data process in the population).	4.5 parsecs away Travel
\|	\|	christopher-beckham.github.io Techniques for label conditioning in Gaussian denoising diffusion models \| Christopher Beckham, PhD	24.7 parsecs away Travel
\|		Techniques for label conditioning in Gaussian denoising diffusion models	24.7 parsecs away Travel