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A Bayesian view of the Stein estimator | Explainaway's Blog
https://explainaway.wordpress.com/2008/11/17/a-bayesian-view-of-the-stein-estimator
Just another WordPress.com weblog. Laquo; To sample or not to sample? Missing values in Gibbs sampling. In this case, sample. A Bayesian view of the Stein estimator. I’ve been attending Statistical Theory, a course in Part III Maths at Cambridge, taught by Richard Samworth. From a frequentist perspective this appears completely unintuitive, but from a Bayesian perspective it appears much more reasonable. Assume we observe vector. Drawn from a multivariate normal of dimension p, with mean. Which is very c...
February | 2009 | Explainaway's Blog
https://explainaway.wordpress.com/2009/02
Just another WordPress.com weblog. Archive for February, 2009. Matlab vs. R performance benchmarking. February 3, 2009. I’ve put a performance comparison of R and Matlab up on my website since I thinking of doing an R tutorial for the group. You can find my table of results, conclusions, and code here. You are currently browsing the Explainaway's Blog. Blog archives for February, 2009. Create a free website or blog at WordPress.com. Follow “Explainaway's Blog”. Get every new post delivered to your Inbox.
OK, Matlab isn’t that weird | Explainaway's Blog
https://explainaway.wordpress.com/2008/12/10/ok-matlab-isnt-that-weird
Just another WordPress.com weblog. Laquo; Matlab is weird. Binomial p-values for comparing pyrosequencing samples. OK, Matlab isn’t that weird. So as Johan pointed out I made a pretty big mistake in my previous post because rand(10000) does not generate 10000 random numbers, it generates a 10000×10000 matrix of random numbers. Here’s the corrected code:. And here are the corrected times:. This entry was posted on December 10, 2008 at 2:55 pm and is filed under Uncategorized. Feed You can leave a response.
In this case, sample. | Explainaway's Blog
https://explainaway.wordpress.com/2008/11/25/in-this-case-sample
Just another WordPress.com weblog. Laquo; A Bayesian view of the Stein estimator. 8220;Ridiculous stats”. In this case, sample. Previous post I described the debate over whether to sample or collapse missing observation variables. I have now run the experiments, the results of which I will present here as promised. These experiments are based on my infinite sparse Factor Analysis model, which you can read about here. Figure 4. Boxplot of predictive error with diagonal or isotropic noise model. This entry...
To sample or not to sample? Missing values in Gibbs sampling | Explainaway's Blog
https://explainaway.wordpress.com/2008/11/12/to-sample-or-not-to-sample-missing-values-in-gibbs-sampling
Just another WordPress.com weblog. A Bayesian view of the Stein estimator. To sample or not to sample? Missing values in Gibbs sampling. I ran into an interesting debate last week. Consider the factor analysis model:. Where y is an observed vector, G is the mixing matrix, x is a vector of latent variables and the last term is isotropic Gaussian noise. Assume we observe a sequence of n y’s drawn iid from the model. Then writing. A very naive way to infer the posterior. The second approach is to exclude te...
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The Grad Factor: CBMS highlights
http://factorgrad.blogspot.com/2010/09/cbms-highlights.html
Monday, September 20, 2010. Here are my highlights from CBMS: the non-parametric Bayes conference at UC Santa Cruz. It was organized more like a summer school, however. Michael Jordan gave one lecture where he went through various models various NP Bayes models he has worked with: LDA, IBPs, sticky HMMs, . He didn't get too technical, but tried to give a high level view of many models motivated by applications such as speaker diarization. Wes Johnson gave one lecture giving examples of NP Bayes in biology.
The Grad Factor: MLSP 2010 highlights
http://factorgrad.blogspot.com/2010/09/mlsp-highlights.html
Monday, September 20, 2010. Here are my highlights from MLSP. To my knowledge this was the first machine learning conference to occur within the arctic circle 68 N. The conference took place on top of the gondola. The key highlight of the conference was the summer bobsled track from the conference center to the village. The food was mostly raindeer (in various forms) and berries ;). On the technical side:. Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process. A nice and complete post in ...
The Grad Factor: July 2010
http://factorgrad.blogspot.com/2010_07_01_archive.html
Sunday, July 4, 2010. Why LaTeX is superior to MS Word. Speed: Anything that requires a mouse and clicking through menus will be slower than one where you can write it out in a few key strokes. This means that writing characters with accents, special symbols, and especially equations will be much faster to write in LaTeX. MS Word (and even worse open office) can get sluggish when editing large documents with lots of equations and figures. Security: stored in plain text. You also know the diff tool will s...
The Grad Factor: October 2010
http://factorgrad.blogspot.com/2010_10_01_archive.html
Tuesday, October 19, 2010. Following the well known latex-one-pager. There is now the LaTeX A0. That combines 16 pages of LaTeX summary into poster. Subscribe to: Posts (Atom). View my complete profile.
The Grad Factor: September 2010
http://factorgrad.blogspot.com/2010_09_01_archive.html
Monday, September 20, 2010. Here are my highlights from MLSP. To my knowledge this was the first machine learning conference to occur within the arctic circle 68 N. The conference took place on top of the gondola. The key highlight of the conference was the summer bobsled track from the conference center to the village. The food was mostly raindeer (in various forms) and berries ;). On the technical side:. Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process. Here are my highlights from ...
The Grad Factor: LaTeX A0 Summary
http://factorgrad.blogspot.com/2010/10/latex-a0-summary.html
Tuesday, October 19, 2010. Following the well known latex-one-pager. There is now the LaTeX A0. That combines 16 pages of LaTeX summary into poster. Subscribe to: Post Comments (Atom). View my complete profile.
The Grad Factor: July 2012
http://factorgrad.blogspot.com/2012_07_01_archive.html
Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a "health warning" for methodology he says he sees many neuroscientists use. Then you ca...
The Grad Factor: Eureka
http://factorgrad.blogspot.com/2010/12/eureka.html
Monday, December 27, 2010. Undirected Grad has a blog post. About symbolic regression using a new peice of software called Eureka. I have tried it out and it is pretty effective at uncovering the latent function from the synthetic experiments a tried, such as y = logistic(x 2 sin(x) . Subscribe to: Post Comments (Atom). View my complete profile.
The Grad Factor: September 2008
http://factorgrad.blogspot.com/2008_09_01_archive.html
Saturday, September 20, 2008. While at Google at got exerience with code reviews. Code readability and style is taken very seriously there and can drag out code reviews. So, I looked back at comments I received in our web integrated revision control system to see what my common issues where. I found a lot of stuff about style one would usually not notice. 80 characters line width. Seems like those 82's always sneak in. Check your code with a linter to find this kind of stuff! Document why your using.
The Grad Factor: NIPS 2011
http://factorgrad.blogspot.com/2012/07/nips-2011.html
Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a "health warning" for methodology he says he sees many neuroscientists use. Then you ca...
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Thesis
Thesis: moving forward faster. The rate at which improvements are propagated determines the rate of progress. The web propagates information almost instantly, but the transfer paradigm has been that of independent learning. The paradigm of teaching is much. The teacher determines the state of knowledge of the student. The teacher supplies delta information. The information can be formatted as words, diagrams, problem-sets, et cetera. There are several possible modes:. A mediating infrastructure with.
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Explainaway's Blog | Just another WordPress.com weblog
Just another WordPress.com weblog. Elastic net, LASSO, and LARS in Python. July 26, 2012. I’m currently looking for implementations of the LASSO. Otherwise known as L1 and L1/L2 regularised linear regression respectively, in Python. The options seem to be scikit.learn. The former offers coordinate ascent or the LARS algorithm coded in pure Python (with Numpy obviously), whereas the latter just wraps Jerome Friedman’s Fortran code from the R glmnet package. Posted in Machine learning. February 3, 2009.
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