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The problem I'm having is that my MAP estimate for theta using PyMC2 is ~0.68 (correct), while the estimate PyMC3 gives is ~0.26 (incorrect). I suspect this has something to do with the way I'm defining the deterministic function. PyMC3 won't let me use a lambda function, so I just have to write the expression in-line.

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May 18, 2017 · Estimating the parameters of Bayesian models has always been hard, impossibly hard actually in many cases for anyone but experts. However, recent advances in probabilistic programming have endowed us with tools to estimate models with a lot of parameters and for a lot of data. In this tutorial, we will discuss two of these tools, PyMC3 and Edward.

This page contains resources about Probabilistic Graphical Models, Probabilistic Machine Learning and Probabilistic Models, including Latent Variable Models. Bayesian and non-Bayesian approaches can either be used. 1 Subfields and Concepts 2 Online Courses 2.1 Video Lectures 2.2 Lecture Notes 3 Books and Book Chapters 4 Scholarly Articles 5 Tutorials 6 Software 7 See also 8 Other Resources See ...

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Jun 28, 2017 · I am trying to use PyMC3 to fit the spectra of galaxies. The model I use to fit the spectra is currently described by four parameters. At present, I am trying to fit simulated spectra (i.e., data) to assess (a) how reliably PyMC3 is able to constrain the known model parameters and (b) how quickly it converges. All the parameters in my model are continuous, so I’m using the NUTS sampler. When ... はじめに 内容 状態空間時系列モデルの概要 状態空間モデルとは何か？ 典型的なモデルの紹介 今後の記事の執筆方針 本記事あわせて全5本で構想中 背景 状態空間モデルは時系列分析でよく用いられる手法の一つ Pythonユーザーにとっては情報が少なくややとっつきにくい Rと比べて書籍が圧倒的 ...

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PyStan. Fast. Awesome documentation. Big and powerful community. Looking forward to PyStan 3.0 which is expected by the end of October and will have faster compilation time, multithreading and GPU support.

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