PyMC Developers

@pymc_devs

Probabilistic Programming in Python

github.com/pymc-devs/pymc3
Vrijeme pridruživanja: ožujak 2017.
6 fotografija ili videozapisa Fotografije i videozapisi

Tweetovi

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  1. Prikvačeni tweet
    29. svi 2019.

    PyMC3 3.7 is released! Highlights: - Python 3 only - for plotting - Data class for handling changing data between inference and posterior predictive - Big under the hood improvements, especially to prior predictive sampling and shape handling

    1 reply 34 proslijeđena tweeta 108 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    21. svi 2019.

    We added a bunch of new features to ArivZ, like rankplot, new ess, and others. Update your priors of how awesome ArviZ is by updating your environment

    ArviZ 0.4.0 is released on pypi, and for the first time on conda-forge! conda install -c conda-forge arviz pip install arviz
    1 reply 7 proslijeđenih tweetova 23 korisnika označavaju da im se sviđa
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  3. 21. svi 2019.

    Excited to welcome Luciano Paz and Robert Goldman to the PyMC core developer team! Both have made many meaningful contributions to the upcoming 3.7 release.

    7 proslijeđenih tweetova 26 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    7. ožu 2019.

    Loving the Gaussian Processes support in . Thanks !!

    2 proslijeđena tweeta 3 korisnika označavaju da im se sviđa
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  5. proslijedio/la je Tweet
    10. ožu 2019.

    I recently programmed a perception experiment, sampled from hierarchical Bayesian models, talked via USB to a device (lacking a proper driver), scripted 3D animations & read accelerometer data via i2c. Thanks !!! (& & & & & ...)

    2 proslijeđena tweeta 7 korisnika označava da im se sviđa
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  6. proslijedio/la je Tweet
    13. ožu 2019.

    is a -based statistical modeling tool for Bayesian statistical modeling & Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Sponsored Project since 2016

    11 proslijeđenih tweetova 18 korisnika označava da im se sviđa
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  7. 22. ožu 2019.

    We just crossed 4000 stars and 1000 forks on github. Thanks to all our awesome users and everyone who contributed! Next up: new release (coming soon) and continued work on our prototype.

    10 proslijeđenih tweetova 43 korisnika označavaju da im se sviđa
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  8. proslijedio/la je Tweet
    15. sij 2019.

    Excited to dig into my newest book

    3 proslijeđena tweeta 20 korisnika označava da im se sviđa
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  9. proslijedio/la je Tweet
    22. sij 2019.

    More robust ADVI in PyMC3? ==> Partial port of „Yes but did it work?: Evaluating Variational Inference” to PyMC3 ().

    3 proslijeđena tweeta 15 korisnika označava da im se sviđa
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  10. proslijedio/la je Tweet
    24. sij 2019.

    is a -based statistical modeling tool for Bayesian statistical modeling & Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Sponsored Project since 2016

    17 proslijeđenih tweetova 36 korisnika označava da im se sviđa
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  11. proslijedio/la je Tweet
    28. sij 2019.

    Always forgetting the nuance between likelihood and probability? Good news I wrote a Bayesian glossary to help you navigate Bayesian papers and blog posts

    16 proslijeđenih tweetova 60 korisnika označava da im se sviđa
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  12. 30. sij 2019.

    Reported 9x speed-up of sampling a hierarchical multinomial model on 9 GPUs vs 32 CPUs What were your experiences with running on the ?

    5 proslijeđenih tweetova 18 korisnika označava da im se sviđa
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  13. proslijedio/la je Tweet
    31. pro 2018.

    Advance Bayesian Modelling with PyMC3 😄

    4 proslijeđena tweeta 25 korisnika označava da im se sviđa
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  14. proslijedio/la je Tweet
    30. srp 2018.

    All the examples in my book have been translated into both brms/tidyverse and PyMC3. Everything linked at top here:

    Summer project No. 1: Go through this bad baby by as reexpressed by using and packages
    9 replies 186 proslijeđenih tweetova 753 korisnika označavaju da im se sviđa
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  15. proslijedio/la je Tweet
    29. pro 2018.

    A simple application of Probabilistic Programming with PyMC3 in Python

    1 reply 59 proslijeđenih tweetova 95 korisnika označava da im se sviđa
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  16. proslijedio/la je Tweet
    26. pro 2018.

    The second edition of Bayesian Analysis with Python is here! Thanks to for his great foreword, thanks , and Bill Engels for reviewing. Also thanks to and for their dedication and hard work.

    5 replies 55 proslijeđenih tweetova 200 korisnika označava da im se sviđa
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  17. proslijedio/la je Tweet
    31. pro 2018.

    is a -based statistical modeling tool for Bayesian statistical modeling & Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Sponsored Project since 2016

    23 proslijeđena tweeta 47 korisnika označava da im se sviđa
    Poništi
  18. 2. sij 2019.

    We dropped Python 2 support already (on master) thanks to

    Are we allowed to drop Python 2 now?
    6 proslijeđenih tweetova 25 korisnika označava da im se sviđa
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  19. 21. pro 2018.

    We also had a great code sprint in London last week and made some tangible progress. Probability gives us a huge head start and allows us to really focus on API design. Thanks for joining and to for sponsoring us!

    12 proslijeđenih tweetova 41 korisnik označava da mu se sviđa
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  20. 21. pro 2018.

    3.6 released! This release comes with new features and bugfixes. The main highlight is our shiny new website courtesy of . This will also be the last Python 2 compatible release. Update via pip or conda-forge.

    26 proslijeđenih tweetova 64 korisnika označavaju da im se sviđa
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