/explore
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www.ericekholm.com | ||
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www.cedricscherer.com
A step-by-step tutorial explaining how my visualizations have evolved from a typical basic ggplot. Here, I transform a basic boxplot into a compelling and self-explanatory combination of a jittered dot strip plot and a lollipop plot. |
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www.nicholas-ollberding.com
Inherent limitations with one-at-a-time (OaaT) feature testing (i.e., single feature differential abundance analysis) have contributed to the increasing popularity of mixture models for correlating microbial features with factors of interest (i. |
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www.rdatagen.net
Inspired by a free online course titled Complier Average Causal Effects (CACE) Analysis and taught by Booil Jo and Elizabeth Stuart (through Johns Hopkins University), I've decided to explore the topic a little bit. My goal here isn't to explain CACE analysis in extensive detail (you should definitely go take the course for that), but to describe the problem generally and then (of course) simulate some data. A plot of the simulated data gives a sense of what we are estimating and assuming. |
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yang-song.net
This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... |
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