andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2067 knowledge-graph by maker-knowledge-mining
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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.
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1 Here are X’s comments on a paper, “Expectation-Propagation for Likelihood-Free Inference,” by Simon Barthelme and Nicolas Chopin. [sent-2, score-0.102]
2 The paper is not new but the topic is still hot. [sent-3, score-0.437]
3 Also there’s this paper by Maurizio Filippone and Mark Girolami on computation for Gaussian process models. [sent-4, score-0.504]
4 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. [sent-5, score-0.918]
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same-blog 1 1.0000001 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.
2 0.27090484 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
3 0.26975209 2139 andrew gelman stats-2013-12-19-Happy birthday
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 .
4 0.17415622 869 andrew gelman stats-2011-08-24-Mister P in Stata
Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
5 0.14370716 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: 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.
2 0.69085437 904 andrew gelman stats-2011-09-13-My wikipedia edit
Introduction: The other day someone mentioned my complaint about the Wikipedia article on “Bayesian inference” (see footnote 1 of this article ) and he said I should fix the Wikipedia entry myself. And so I did . I didn’t have the energy to rewrite the whole article–in particular, all of its examples involve discrete parameters, whereas the Bayesian problems I work on generally have continuous parameters, and its “mathematical foundations” section focuses on “independent identically distributed observations x” rather than data y which can have different distributions. It’s just a wacky, unbalanced article. But I altered the first few paragraphs to get rid of the stuff about the posterior probability that a model is true. I much prefer the Scholarpedia article on Bayesian statistics by David Spiegelhalter and Kenneth Rice, but I couldn’t bring myself to simply delete the Wikipedia article and replace it with the Scholarpedia content. Just to be clear: I’m not at all trying to disparage
3 0.68282402 2144 andrew gelman stats-2013-12-23-I hate this stuff
Introduction: Aki pointed me to this article . I’m too exhausted to argue all this in detail yet one more time, but let me just say that I hate this stuff for the reasons given in Section 5 of this paper from 1998 (based on classroom activities from 1994). I’ve hated this stuff for a long time. And I don’t think Yitzhak likes it either; see this discussion from 2005 and this from 2009.
4 0.66294217 1443 andrew gelman stats-2012-08-04-Bayesian Learning via Stochastic Gradient Langevin Dynamics
Introduction: Burak Bayramli writes: In this paper by Sunjin Ahn, Anoop Korattikara, and Max Welling and this paper by Welling and Yee Whye The, there are some arguments on big data and the use of MCMC. Both papers have suggested improvements to speed up MCMC computations. I was wondering what your thoughts were, especially on this paragraph: When a dataset has a billion data-cases (as is not uncommon these days) MCMC algorithms will not even have generated a single (burn-in) sample when a clever learning algorithm based on stochastic gradients may already be making fairly good predictions. In fact, the intriguing results of Bottou and Bousquet (2008) seem to indicate that in terms of “number of bits learned per unit of computation”, an algorithm as simple as stochastic gradient descent is almost optimally efficient. We therefore argue that for Bayesian methods to remain useful in an age when the datasets grow at an exponential rate, they need to embrace the ideas of the stochastic optimiz
5 0.65788376 1251 andrew gelman stats-2012-04-07-Mathematical model of vote operations
Introduction: Alex points me to this paper by Lirong Xia. With its model of independent identically distributed votes, the paper is a bit theoretical for my taste, but maybe it will interest some of you.
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same-blog 1 0.88732702 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.
2 0.88499856 1589 andrew gelman stats-2012-11-25-Life as a blogger: the emails just get weirder and weirder
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
4 0.8583436 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
5 0.85376358 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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