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 austinrochford.com
Bayesian Age/Period/Cohort Models in Python with PyMC | Austin Rochford
| | twiecki.io
whilemy_mcmc:gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC
1.6 parsecs away

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| | [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library.
| | www.djmannion.net
Fitting models with circular data in PyMC
2.8 parsecs away

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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.
| | ckrapu.github.io
Don't know where your data is from? Bayesian modeling for unknown coordinates | Christopher Krapu
2.2 parsecs away

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| | A PyMC Gaussian process example with uncertain spatial coordinates
| | blog.otoro.net
Neural Network Evolution Playground with Backprop NEAT |
19.1 parsecs away

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| [AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ...