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isaacslavitt.com | ||
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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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austinrochford.com
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| | | | | Splines are a powerful tool when modeling nonlinear relationships. This post shows how to include splines in a Bayesian model in Python using pymc3. In addition, we will show how to use a second splin | |
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simkovic.github.io
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| | | | | [AI summary] This post discusses the limitations of using raw score differences in ordinal data analysis, particularly when dealing with ceiling effects. The author demonstrates that raw score differences can be biased towards zero and have reduced precision in boundary regions. They advocate for using logit-based models to accurately estimate treatment effects while accounting for ordinal data structure and ceiling effects. The post includes simulations showing how ceiling effects can reduce the detectability of true effects and highlights the importance of using appropriate statistical models to avoid biased conclusions. | |
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nickhar.wordpress.com
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| | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | ||
