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On Markov chain Monte Carlo methods for tall data

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Figure

Figure 1: Pseudocode of the MH algorithm targeting the distribution π. Note that π isonly involved in ratios, so that one only needs to know an unnormalized versionγ of π.
Figure 2: Results of 10 000 iterations of vanilla MH fitting a Gaussian model to one-dimensional Gaussian and lognormal synthetic data, on the left and right panel,respectively.Figures 2(a) and 2(b) show the chain histograms, joint andmarginals; the x-axis
Figure 3: Results of 10 000 iterations of Firefly MH (MacLaurin and Adams, 2014) on ourGaussian and lognormal running examples
Figure 4: Results of SGLD (Welling and Teh, 2011) on our Gaussian and lognormal runningexamples
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