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dustintran.com | ||
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xcorr.net
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| | | | | Log determinants frequently need to be computed as part of Bayesian inference in models with Gaussian priors or likelihoods. They can be quite troublesome to compute; done through the Cholesky decomposition, it's an $latex \mathcal{O}(N^3)$ operation. It's pretty much infeasible to compute the exact log det for arbitrary matrices > 10,000 x 10,000. Models with... | |
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www.djmannion.net
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| | | | | Data are sometimes on a circular scale, such as the angle of an oriented stimulus, and the analysis of such data often needs to take this circularity into account. Here, we will look at how we can use PyMC to fit a model to circular data. | |
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tiao.io
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| | | | | A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework. | |
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hbiostat.org
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| | | This article provides a demonstration that the perceived non-robustness of nonlinear models for covariate adjustment in randomized trials may be less of an issue than the non-transportability of marginal so-called robust estimators. | ||
