andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1856 knowledge-graph by maker-knowledge-mining
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Introduction: I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. So here goes. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. We can actually now fit Gaussian processes in Stan . But for big problems (or even moderately-sized problems), full Bayes can be slow. GPstuff uses EP, which is faster. At some point we’d like to implement EP in Stan. (Right now we’re working with Dave Blei to implement VB.) GPstuff really works. I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. He was sitting in his office and just whip
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1 I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. [sent-1, score-0.816]
2 Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. [sent-3, score-0.638]
3 The tools include, among others, various inference methods, sparse approximations and model assessment methods. [sent-4, score-0.672]
4 We can actually now fit Gaussian processes in Stan . [sent-5, score-0.326]
5 But for big problems (or even moderately-sized problems), full Bayes can be slow. [sent-6, score-0.151]
6 At some point we’d like to implement EP in Stan. [sent-8, score-0.206]
7 (Right now we’re working with Dave Blei to implement VB. [sent-9, score-0.206]
8 I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. [sent-11, score-0.415]
9 He was sitting in his office and just whipped up the model and fit it. [sent-12, score-0.575]
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same-blog 1 1.0 1856 andrew gelman stats-2013-05-14-GPstuff: Bayesian Modeling with Gaussian Processes
Introduction: I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. So here goes. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. We can actually now fit Gaussian processes in Stan . But for big problems (or even moderately-sized problems), full Bayes can be slow. GPstuff uses EP, which is faster. At some point we’d like to implement EP in Stan. (Right now we’re working with Dave Blei to implement VB.) GPstuff really works. I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. He was sitting in his office and just whip
2 0.27090484 2067 andrew gelman stats-2013-10-18-EP and ABC
Introduction: Expectation propagation and approximate Bayesian computation. Here are X’s comments on a paper, “Expectation-Propagation for Likelihood-Free Inference,” by Simon Barthelme and Nicolas Chopin. The paper is not new but the topic is still hot. Also there’s this paper by Maurizio Filippone and Mark Girolami on computation for Gaussian process models. I wonder how this connects to GPstuff , which I think is what Aki did to fit the birthdays model: This stuff is where it’s at.
3 0.18247622 1384 andrew gelman stats-2012-06-19-Slick time series decomposition of the birthdays data
Introduction: Aki updates : Here is my plot using the full time series data to make the model. Data analysis could be made in many different ways, but my hammer is Gaussian process, and so I modeled the data with a Gaussian process with six components 1) slowly changing trend 2) 7 day periodical component capturing day of week effect 3) 365.25 day periodical component capturing day of year effect 4) component to take into account the special days and interaction with weekends 5) small time scale correlating noise 6) independent Gaussian noise - Day of the week effect has been increasing in 80′s - Day of year effect has changed only a little during years - 22nd to 31st December is strange time I [Aki] will make the code available this week, but we have to first make new release of our GPstuff toolbox, as I used our development code to do this. I have no idea what’s going on with 29 Feb; I wouldn’t see why births would be less likely on that day. Also, the above graphs are g
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Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
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Introduction: Aki writes: Here’s my version of the birthday frequency graph . I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan. ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code? P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try. P.P.S. More here .
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Introduction: I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. So here goes. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. We can actually now fit Gaussian processes in Stan . But for big problems (or even moderately-sized problems), full Bayes can be slow. GPstuff uses EP, which is faster. At some point we’d like to implement EP in Stan. (Right now we’re working with Dave Blei to implement VB.) GPstuff really works. I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. He was sitting in his office and just whip
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Introduction: Over the past few months, we’ve talked about modeling with particle physicists ( Allen Caldwell ), astrophysicists ( David Hogg , who regularly comments here), and climate and energy usage modelers ( Phil Price , who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that are hugely complex and provided by outside implementations from labs like CERN or LBL. Some concrete examples for energy modeling are the TOUGH2 thermal simulator, the EnergyPlus building energy usage simulator, and global climate model (GCM) implementations. These models have the character of not only being black boxes, but taking several seconds or more to generate the equivalent of a likelihood function evaluation. So we can’t use something like Stan, because nobody has the person years required to implement something like TOUGH2 in Stan (and Stan doesn’t have the debugging or modularity tools to suppor
Introduction: David Duvenaud writes: I’ve been following your recent discussions about how an AI could do statistics [see also here ]. I was especially excited about your suggestion for new statistical methods using “a language-like approach to recursively creating new models from a specified list of distributions and transformations, and an automatic approach to checking model fit.” Your discussion of these ideas was exciting to me and my colleagues because we recently did some work taking a step in this direction, automatically searching through a grammar over Gaussian process regression models. Roger Grosse previously did the same thing , but over matrix decomposition models using held-out predictive likelihood to check model fit. These are both examples of automatic Bayesian model-building by a search over more and more complex models, as you suggested. One nice thing is that both grammars include lots of standard models for free, and they seem to work pretty well, although the
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Introduction: Visual Economics shows statistics on average food consumption in America: My brief feedback is that water is confounded with these results. They should have subtracted water content from the weight of all dietary items, as it inflates the proportion of milk, vegetable and fruit items that contain more water. They did that for soda (which is represented as sugar/corn syrup), amplifying the inconsistency. Time Magazine had a beautiful gallery that visualizes diets around the world in a more appealing way.
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Introduction: In the email the other day, subject line “Casting blogger, writer, journalist to host cable series”: Hi there Andrew, I’m casting a male journalist, writer, blogger, documentary filmmaker or comedian with a certain type personality for a television pilot along with production company, Pipeline39. See below: A certain type of character – no cockiness, no ego, a person who is smart, savvy, dry humor, but someone who isn’t imposing, who can infiltrate these organizations. This person will be hosting his own show and covering alternative lifestyles and secret societies around the world. If you’re interested in hearing more or would like to be considered for this project, please email me a photo and a bio of yourself, along with contact information. I’ll respond to you ASAP. I’m looking forward to hearing from you. *** Casting Producer (646) ***.**** ***@gmail.com I was with them until I got to the “no ego” part. . . . Also, I don’t think I could infiltrate any org
Introduction: Tapen Sinha writes: Living in Mexico, I have been witness to many strange (and beautiful) things. Perhaps the strangest happened during the first outbreak of A(H1N1) in Mexico City. We had our university closed, football (soccer) was played in empty stadiums (or should it be stadia) because the government feared a spread of the virus. The Metro was operating and so were the private/public buses and taxis. Since the university was closed, we took the opportunity to collect data on facemask use in the public transport systems. It was a simple (but potentially deadly!) exercise in first hand statistical data collection that we teach our students (Although I must admit that I did not dare sending my research assistant to collect data – what if she contracted the virus?). I believe it was a unique experiment never to be repeated. The paper appeared in the journal Health Policy. From the abstract: At the height of the influenza epidemic in Mexico City in the spring of 2009, the f
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Introduction: I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. So here goes. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. We can actually now fit Gaussian processes in Stan . But for big problems (or even moderately-sized problems), full Bayes can be slow. GPstuff uses EP, which is faster. At some point we’d like to implement EP in Stan. (Right now we’re working with Dave Blei to implement VB.) GPstuff really works. I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. He was sitting in his office and just whip
5 0.85755157 1677 andrew gelman stats-2013-01-16-Greenland is one tough town
Introduction: Americans (including me) don’t know much about other countries. Jeff Lax sent me to this blog post by Myrddin pointing out that Belgium has a higher murder rate than the rest of Western Europe. I have no particular take on this, but it’s a good reminder that other countries differ from each other. Here in the U.S., we tend to think all western European countries are the same, all eastern European countries are the same, etc. In reality, Sweden is not Finland . P.S. According to the Wiki , Greenland is one tough town. I guess there’s nothing much to do out there but watch satellite TV, chew the blubber, and kill people.
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