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<div style="margin: 0px; color: black; background-color: white;"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">We have an S3RI seminar today (</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; background-color: white; font-size: 12pt;">at
 2 pm), where</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; background-color: white;"><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">&nbsp;Dr&nbsp;Sara Wade&nbsp;</span></span><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">(University
 of Edinburgh) is speaking to us on</span><br>
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<p style="margin-top: 0px; margin-bottom: 0px; background-color: white;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: black;">&quot;</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: black;">MCMC
 for variationally sparse Gaussian processes: A pseudo-marginal approach</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: black;"><span style="margin:0px; box-sizing:border-box"></span></span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: black;">&quot;</span><span style="margin: 0px; font-size: 12pt; font-family: &quot;Times New Roman&quot;, serif; color: black;"></span></p>
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<p style="margin-top: 0px; margin-bottom: 0px; background-color: white;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Please see below for the details of the talk and how to
 join the seminar online.</span><span style="margin: 0px; font-size: 12pt; font-family: &quot;Times New Roman&quot;, serif; color: black;"></span></p>
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<p style="margin-top: 0px; margin-bottom: 0px; background-color: white;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Hope to see you at the seminar later today!</span><span style="margin: 0px; font-size: 12pt; font-family: &quot;Times New Roman&quot;, serif; color: black;"></span></p>
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<p style="margin-top: 0px; margin-bottom: 0px; background-color: white;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Kind regards,</span><span style="margin: 0px; font-size: 12pt; font-family: &quot;Times New Roman&quot;, serif; color: black;"></span></p>
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</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: black; background-color: white;">Jessie</span></p>
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<span style="font-size:12pt"><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif"><em><strong>On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach</strong></em></span></span></div>
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<span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Sara Wade (University of Edinburgh)</span><br>
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<span style="font-size:12pt"><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Abstract: Gaussian processes (GPs) are frequently used in machine learning and statistics to construct powerful models. However, when employing GPs in practice,
 important considerations must be made, regarding the high computational burden, approximation of the posterior, choice of the covariance function and inference of its hyperparmeters. To address these issues, Hensman et al. (2015) combine variationally sparse
 GPs with Markov chain Monte Carlo (MCMC) to derive a scalable, flexible and general framework for GP models. Nevertheless, the resulting approach requires intractable likelihood evaluations for many observation models. To bypass this problem, we propose a
 pseudo-marginal (PM) scheme that offers asymptotically exact inference as well as computational gains through doubly stochastic estimators for the intractable likelihood and large datasets. In complex models, the advantages of the PM scheme are particularly
 evident, and we demonstrate this on a two-level GP regression model with a nonparametric covariance function to capture non-stationarity.</span></span></div>
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<span style="margin: 0px; color: rgb(117, 123, 128); font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;">Chieh-Hsi (Jessie) Wu, PhD</span></p>
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<span style="margin: 0px; color: rgb(117, 123, 128); font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;">Lecturer in Statistics</span></p>
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<span style="color: rgb(117, 123, 128); font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;">School of Mathematics</span></p>
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<span style="margin: 0px; color: rgb(117, 123, 128); font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;">University of Southampton</span></p>
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