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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Calibrated approximate Bayesian inference
Published in Proceedings of the 36th International Conference on Machine Learning, 2019
We give a general purpose computational framework for estimating the bias in coverage resulting from making approximations in Bayesian inference. Coverage is the probability credible sets cover true parameter values. We show how to estimate the actual coverage an approximation scheme achieves when the ideal observation model and the prior can be simulated, but have been replaced, in the Monte Carlo, with approximations as they are intractable. Coverage estimation procedures given in Lee et al.(2018) work well on simple problems, but are biased, and do not scale well, as those authors note. For example, the methods of Lee et al.(2018) fail for calibration of an approximate completely collapsed MCMC algorithm for partition structure in a Dirichlet process for clustering group labels in a hierarchical model. By exploiting the symmetry of the coverage error under permutation of low level group labels and smoothing with Bayesian Additive Regression Trees, we are able to show that the original approximate inference had poor coverage and should not be trusted.
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Distortion estimates for approximate Bayesian inference
Published in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, 2020
Current literature on posterior approximation for Bayesian inference offers many alternative methods. Does our chosen approximation scheme work well on the observed data? The best existing generic diagnostic tools treating this kind of question by looking at performance averaged over data space, or otherwise lack diagnostic detail. However, if the approximation is bad for most data, but good at the observed data, then we may discard a useful approximation. We give graphical diagnostics for posterior approximation at the observed data. We estimate a “distortion map” that acts on univariate marginals of the approximate posterior to move them closer to the exact posterior, without recourse to the exact posterior.
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Improving bridge estimators via f-GAN
Published in Statistics and Computing, 2022
Bridge sampling is a powerful Monte Carlo method for estimating ratios of normalizing constants. Various methods have been introduced to improve its efficiency. These methods aim to increase the overlap between the densities by applying appropriate transformations to them without changing their normalizing constants. In this paper, we first give a new estimator of the asymptotic relative mean square error (RMSE) of the optimal Bridge estimator by equivalently estimating an f-divergence between the two densities. We then utilize this framework and propose f-GAN-Bridge estimator (f-GB) based on a bijective transformation that maps one density to the other and minimizes the asymptotic RMSE of the optimal Bridge estimator with respect to the densities. This transformation is chosen by minimizing a specific f-divergence between the densities. We show f-GB is optimal in the sense that within any given set of candidate transformations, the f-GB estimator can asymptotically achieve an RMSE lower than or equal to that achieved by Bridge estimators based on any other transformed densities. Numerical experiments show that f-GB outperforms existing methods in simulated and real-world examples. In addition, we discuss how Bridge estimators naturally arise from the problem of f-divergence estimation.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
SB2.1 Foundations of Statistical Inference
, Department of Statistics, University of Oxord, 1900
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title: “Teaching experience 1” collection: teaching type: “Undergraduate course” permalink: /teaching/2014-spring-teaching-1 venue: “University 1, Department” date: 2014-01-01 location: “City, Country” —