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StanConnect 2021: Ecology (part 2)
StanConnect is a minisymposium series focusing on Stan and Bayesian analysis. The current recording is part 2 of the two-part Ecology session, hosted by Dr. Jacob B. Socolar on Oct. 4, 2021.
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Відео

StanConnect 2021: Ecology (part 1).
Переглядів 4592 роки тому
StanConnect is a virtual miniseries focusing on Stan & Bayesian inference. The current recording is part 1 of a two-part ecology session, hosted by Jacob B. Socolar on Sep 30, 2021.
StanConnect 2021: biomedical science
Переглядів 3282 роки тому
StanConnect is a virtual miniseries focus on Stan & Bayesian inference. The biomedical science session is hosted by Dr. Luiz Max Carvalho on Oct 19, 2021.
StanConnect 2021: cognitive science
Переглядів 4882 роки тому
StanConnect is a virtual miniseries focusing on Stan & Bayesian inference. The current session of cognitive science is hosted by Dr. Bruno Nicenboim on Nov. 19, 2021.
Intro to gaussian processes in Stan: Finding exoplanets
Переглядів 8 тис.4 роки тому
Welcome to the official Stan youtube channel! Stan is a state-of-the-art probabilistic programming language. Here we will be releasing new videos regularly covering everything from how to use Stan to bayesian statistics. In this video, you will learning about gaussian processes - what they are and how to implement them in Stan. As an example we will be taking time series data from NASA's Kepler...
StanCon 2020. Talk 17: Ryan Giordano. Effortless frequentist covariances of posterior expectations
Переглядів 6924 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Ryan Giordano, Tamara Brodercik, MIT Effortless frequentist covariances of posterior expectations in Stan The frequentist variability of Bayesian posterior expectations can provide meaningful measures of uncertainty even when models are misspecified, but classical Gaussian approximations based on the maximum a posteriori estima...
StanCon 2020. Talk 13: Cristina Barber. Spatial models for plant neighborhood dynamics in Stan
Переглядів 6894 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Cristina Barber, Andrii Zaiats, Cara Applestein, Trevor Caughlin, Boise State University Spatial models for plant neighborhood dynamics in Stan Spatial interactions between neighboring plants are foundational to population and community ecology. Neighbor interactions include both positive (facilitation) and negative (competitio...
StanCon 2020. Talk 15 (English): Ravin Kumar. ArviZ, InferenceData, and NetCDF
Переглядів 1,2 тис.4 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Ravin Kumar, Oriol Abril-Pla, Osvaldo Martin, Piyush Gautam, Ari Hartikainen, Alexandre Andorra, ArviZ ArviZ, InferenceData, and NetCDF: A unified file format for Bayesians Recently, many libraries have been built to specify probabilistic models as executable code. ArviZ aims to unify common Bayesian statistical analysis by pro...
StanCon 2020. Developer Talk 2: Sebastian Weber. Scaling performance with `reduce_sum` in practice
Переглядів 5364 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Sebastian Weber, Ben Bales, Steve Bronder, Mitzi Morris, Rok Češnovar, Imperial College of London COVID-19 team, Novartis Pharma AG, Basel, Switzerland, Columbia University, University of Ljubljana Scaling Stan's performance with `reduce_sum` in practice While Stan is awesome for writing models, as the size of the data or compl...
StanCon 2020. Talk 4: Witold Wiecek. baggr: a new meta-analysis package using Stan
Переглядів 7334 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Witold Wiecek and Rachael Meager, London School of Economics baggr: a new meta-analysis package using Stan Baggr is a new Bayesian evidence aggregation and meta-analysis package for R using Stan, available on CRAN. The tool provides a user-friendly way to build and criticise models for researchers who want to use Bayesian metho...
StanCon 2020 Virtual Conference: Live Session 1
Переглядів 2,2 тис.4 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 This was the first of three sessions which includes welcome remarks, a plenary talk, and six discussions. Chairs: Simon Maskell (University of Liverpool), Aki Vehtari (Aalto University) * Plenary: Hierarchical Models for Covid - identifying effects of lockdown and an R package, Seth Flaxman, Imperial College, London * Hierarchi...
StanCon 2020. Talk 9: Arman Oganisian. Bayesian Causal Effect Estimation with Stan
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mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Arman Oganisian, Jason A. Roy, University of Pennsylvania Bayesian Causal Effect Estimation with Stan: Parametric and Nonparametric Approaches Bayesian modeling techniques have been growing in popularity within the causal literature. In this talk, we highlight the unique benefits Bayes brings to the table when estimating causal...
StanCon 2020. Talk 15 (Catalan): Oriol Abril Pla. ArviZ, InferenceData, and NetCDF
Переглядів 1514 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Ravin Kumar, Oriol Abril-Pla, Osvaldo Martin, Piyush Gautam, Ari Hartikainen, Alexandre Andorra, ArviZ ArviZ, InferenceData, and NetCDF: A unified file format for Bayesians En els darrers anys s’han creat moltes llibreries per tal de traslladar models probabilístics a codi executable. ArviZ aspira a unificar les tasques d’anàli...
StanCon 2020. Code of Conduct working group update
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mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Lauren Kennedy and Steve Bronder Code of Conduct working group update
StanCon 2020. Talk 6: Sarah Heaps. Enforcing stationarity thru the prior in vector autoregressions
Переглядів 6924 роки тому
StanCon 2020. Talk 6: Sarah Heaps. Enforcing stationarity thru the prior in vector autoregressions
StanCon 2020. Talk 16: Edwin Ng. Orbit: Probabilistic Forecast with Exponential Smoothing
Переглядів 1,4 тис.4 роки тому
mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 Edwin Ng, Zhishi Wang, Steve Yang, Uber Orbit: Probabilistic Forecast with Exponential Smoothing Time series forecast is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some of those forecasting challenges, statistical methods rem...
StanCon 2020 Virtual Conference: Live Session 3
Переглядів 2 тис.4 роки тому
StanCon 2020 Virtual Conference: Live Session 3
StanCon 2020. Developer Talk 3: Steve Bronder. The State of GPU Computation Support for Stan.
Переглядів 5654 роки тому
StanCon 2020. Developer Talk 3: Steve Bronder. The State of GPU Computation Support for Stan.
StanCon 2020 Virtual Conference: Live Session 2
Переглядів 6664 роки тому
StanCon 2020 Virtual Conference: Live Session 2
StanCon 2020. Talk 7: Wade Brorsen. Using Stan for Spatial Smoothing of Regression Parameters
Переглядів 6254 роки тому
StanCon 2020. Talk 7: Wade Brorsen. Using Stan for Spatial Smoothing of Regression Parameters
StanCon 2020. Talk 14: Damjan Vukcevic. Stress-testing the Dawid-Skene model
Переглядів 4324 роки тому
StanCon 2020. Talk 14: Damjan Vukcevic. Stress-testing the Dawid-Skene model
StanCon 2020. Talk 10: Matthew Kay. Building effective uncertainty visualizations
Переглядів 2,6 тис.4 роки тому
StanCon 2020. Talk 10: Matthew Kay. Building effective uncertainty visualizations
StanCon 2020. Talk 2: Jerzy Baranowski. Process fault detection using Stan
Переглядів 5964 роки тому
StanCon 2020. Talk 2: Jerzy Baranowski. Process fault detection using Stan
StanCon 2020. Developer Talk 4: Charles Margossian. Approximate Bayesian inference for latent GPs
Переглядів 5274 роки тому
StanCon 2020. Developer Talk 4: Charles Margossian. Approximate Bayesian inference for latent GPs
StanCon 2020. Talk 11: Jonathan Sidi. API for iterative interrogation of stanfit objects in R
Переглядів 2034 роки тому
StanCon 2020. Talk 11: Jonathan Sidi. API for iterative interrogation of stanfit objects in R
StanCon 2020. Talk 15 (Finnish): Ari Hartikainen. ArviZ, InferenceData, and NetCDF
Переглядів 1184 роки тому
StanCon 2020. Talk 15 (Finnish): Ari Hartikainen. ArviZ, InferenceData, and NetCDF
StanCon 2020. Talk 12: Ben Goodrich. Bayesian Inference without Probability Density Functions
Переглядів 1,9 тис.4 роки тому
StanCon 2020. Talk 12: Ben Goodrich. Bayesian Inference without Probability Density Functions
StanCon 2020. Talk 15 (French): Alexandre Andorra. ArviZ, InferenceData, and NetCDF
Переглядів 1684 роки тому
StanCon 2020. Talk 15 (French): Alexandre Andorra. ArviZ, InferenceData, and NetCDF
StanCon 2020. Talk 1: Brynjólfur Gauti Jónsson. Predicting diagnosed COVID19 cases
Переглядів 8564 роки тому
StanCon 2020. Talk 1: Brynjólfur Gauti Jónsson. Predicting diagnosed COVID19 cases
StanCon 2020. Stan Governing Body update
Переглядів 984 роки тому
StanCon 2020. Stan Governing Body update

КОМЕНТАРІ

  • @gayathrigirishnair7405
    @gayathrigirishnair7405 6 місяців тому

    Concise and extremely helpful! Thanks a lot!

  • @sgeorg80
    @sgeorg80 7 місяців тому

    👍

  • @AdrianConservation
    @AdrianConservation 11 місяців тому

    Which compilers do you all suggest? Thanks!

  • @ShyamDas999
    @ShyamDas999 Рік тому

    What should be RAM size to install rstan. Do you need more than 4GB RAM to install and use Stan program

  • @albert7075
    @albert7075 Рік тому

    what makes the attack and defence parameters differ across teams when they are draw from the same distribution?

  • @cuysaurus
    @cuysaurus Рік тому

    lol. Nice!

  • @lashlarue7924
    @lashlarue7924 Рік тому

    This is the tool I need... I know it gets poo-poohed by the PhD statis crowd, but frankly those guys aren't practitioners and don't understand how to run a damn warehouse. Thank you for open sourcing this...

  • @ahowl7mx
    @ahowl7mx Рік тому

    PIP3 install pystan does not work anymore

  • @akinwilson329
    @akinwilson329 Рік тому

    Incredible.

  • @richardtamsonmlewangaya3771

    Would someone care to explain where the ' my_model.stan' in the fit line came from? I used everything the same and it does not work for me. The fit code did not work.

  • @prestonbotter8390
    @prestonbotter8390 2 роки тому

    Wow great stuff!!

  • @kseniyanovikova8177
    @kseniyanovikova8177 2 роки тому

    Super useful. Thanks a lot for the effort

  • @thryce82
    @thryce82 2 роки тому

    dont care about stan I just wanted to see cute lady with cool accent. was not disappointed/

  • @stevemason3447
    @stevemason3447 2 роки тому

    How do you do predictions? I am assuming that you need to specify the model type such as "logit" or whatever - but how do you specify the type of model for predictions in the generated quantities block? This is impossible :/

  • @alnilam2151
    @alnilam2151 2 роки тому

    Forecasting possible probables, maybe so maybe knot? Sorry, nothing beats solid footing and, even that's knot the missing variable oddly! Keep working though, I like {STAN} I'm just pretty useless with numbers other than 2count & knot 4get the count! A PROGRAM IS NO PROPHET #justsaying [OI AI IOff}

    • @vaslo4655
      @vaslo4655 7 місяців тому

      This post sounds like the AI used to write it was trained on heroin

    • @alnilam2151
      @alnilam2151 7 місяців тому

      You insult your own; if you have any, intelligence with an assinine comment of that nature! I never liked needles thus, possibly maybe you may be the priq minus the IQ! Put that in your pipe and smoke it Vaslo4655@@vaslo4655

  • @OscarSouth
    @OscarSouth 2 роки тому

    Is this using depreciated syntax? Doesn't run and doesn't seem to match what I found looking in the docs

    • @OscarSouth
      @OscarSouth 2 роки тому

      had to change it up to this then it worked: ``` import stan from pathlib import Path X = np.random.normal(5,1,1000) my_data = {"N": 1000, "X": X} stan_model = Path("models/model.stan").read_text() posterior = stan.build(stan_model, data=my_data) fit = posterior.sample(num_chains=4, num_samples=1000) ```

  • @aramisfarias5316
    @aramisfarias5316 2 роки тому

    Thank you very much. Very helpful.

  • @todorantonijevic4833
    @todorantonijevic4833 3 роки тому

    Why are priors for mu and sigma not explicitly defined?

  • @nathanbarnard7896
    @nathanbarnard7896 3 роки тому

    <3

  • @nathanbarnard7896
    @nathanbarnard7896 3 роки тому

    Great video but Aston Villa are not that good

  • @GQElvie
    @GQElvie 3 роки тому

    I can only assume the 92 to 1 ratio of thumbs up to down is based on her beauty. This did not help at all. I do not even know what to ask. I tried copying what she did. She glossed over compiler. is python itself a compiler?

  • @niclasrauchfu1544
    @niclasrauchfu1544 3 роки тому

    Hello there, I can‘t install Pystan. It show me a very long error code. Has anyone a idea 💡?

    • @davidgibbs614
      @davidgibbs614 3 роки тому

      Try the PyStan github ... they're very responsive to issues.

  • @jeancarlostaipechavez8384
    @jeancarlostaipechavez8384 3 роки тому

    how do i make a bayesian autoregressive vector in stan?

  • @quantfinance5637
    @quantfinance5637 3 роки тому

    really nice!

  • @youngkarl1990
    @youngkarl1990 3 роки тому

    really charming beauty 🤤

  • @TANMAN47TANMAN
    @TANMAN47TANMAN 3 роки тому

    Is this better than r tho

  • @abderrahimoujbih6613
    @abderrahimoujbih6613 3 роки тому

    Thank you very much, very well explained

  • @catalinavaldezvega553
    @catalinavaldezvega553 3 роки тому

    Muy buena tu explicación, saludos desde Bolivia

  • @iandennismiller602
    @iandennismiller602 3 роки тому

    Finally! An epic aesthetic that is appropriately befitting of an epic open source project.

  • @uttasargasingh9911
    @uttasargasingh9911 3 роки тому

    Hello Ma'am, Great Video! Could you explain How do we set priors?

  • @yannick431
    @yannick431 3 роки тому

    can i anylize the behaviour of loona stans with stan?

  • @yannick431
    @yannick431 3 роки тому

    who is your bias?

  • @yannick431
    @yannick431 3 роки тому

    can I install vivi through stan?

  • @yannick431
    @yannick431 3 роки тому

    stan loona, yas mama

  • @espabilate_
    @espabilate_ 3 роки тому

    Amazing videos. Have you thought of creating a patreon?

  • @maximilianrohde2724
    @maximilianrohde2724 3 роки тому

    Great video! How do you animate the base R graphics?

  • @hansmeiser6078
    @hansmeiser6078 4 роки тому

    Way too fast! What about the priors

  • @PuolikuuIrene
    @PuolikuuIrene 4 роки тому

    it's really hard to read the last line of code most of the time cause everytime I pause the subtitles go on top of it

  • @jamescarroll8917
    @jamescarroll8917 4 роки тому

    I tried to extend your linear fit to a polynomial fit for my data, and am struggling with vectorized syntax for the likelihood in the model. I initially tried: y ~ normal(c + p1 * x + p2 * x * x, sigma); //likelihood Which failed with "No matches for: vector * vector" So I tried: y ~ normal(c + p1 * x + p2 * pow(x,2), sigma); //likelihood Which failed with no match for pow vector int. I tried casting 2 to a real, or making a real parameter which is = 2 with no success either. I could do a non-vectorized format, which works, but there ought to be a vectorized way to do this, and I suspect I am just missing something in the syntax. Normally google is my friend, but after about 30 minutes of googling "STAN polynomial model example" and finding the wrong things, I thought I would ask here and see if you can help. Thanks!

  • @zainraza9960
    @zainraza9960 4 роки тому

    Came here for Stan, stayed for Maggie

  • @h.kortajarena8363
    @h.kortajarena8363 4 роки тому

    Hello, when I run a model, ask me to install rtools I have installed and it is not working :(

    • @שירהניב-ט7ל
      @שירהניב-ט7ל 3 роки тому

      you might have a problem with the installation of rtools (i had) i would start in looking it up in your computer (i discovered i had two version of rtools installed and that caused the problem i encountered)

  • @its_the_yogi
    @its_the_yogi 4 роки тому

    Great video

  • @tijoalca
    @tijoalca 4 роки тому

    Super creative remote talk! Congratulations to Cristina Barber!

  • @normandelingpole
    @normandelingpole 4 роки тому

    These videos are an amazing resource for a newcomer to the Bayes/HMC/Stan camp, along with the amazing Stan documentation and case studies. Thank you for those!

  • @dynamis38
    @dynamis38 4 роки тому

    Really helpful, easy to follow, and relevant. Thank you!

  • @anassheashaey
    @anassheashaey 4 роки тому

    Thanks for this. Can you tell us what software you used to create the animation in the background? They are so smooth.

  • @waynekenney9311
    @waynekenney9311 4 роки тому

    (Link to slides) avehtari.github.io/modelselection/