I received this email last week from Ian Langmore, a postdoc in Columbia: I’m looking for literature on a subject and can’t find it:  I have a Metropolis sampler where the acceptance probability is evaluated with some error.  This error is not simply error in evaluation of the ... [Read more...]

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After posting so many entries about typos in my books (making you wonder how there could be any text left!) and postponing their classification for so long, I decided on Saturday afternoon to collect those entries into a comprehensive pdf document that should be more useful for readers. I incidentally ... [Read more...]

Thanks to this post, I found OpenClassroom. In addition, thanks to Andrew Ng and his lectures, I took my first course in machine learning. These videos are quite easy to follow. Exercise 2 requires implementing gradient descent algorithm to model data with linear regression. Read More: 243 Words Totally [Read more...]

For its 20th anniversary, JCGS offers free access to papers, including Andrew’s discussion paper Why tables are really much better than graphs. (Another serious ending for an April fool joke!) Incidentally (or rather coincidentally), I received today the great news that our Using parallel computation to improve Independent Metropolis-Hastings ... [Read more...]

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Exercise 5.2 Improves the Logistic Regression implementation done in Exercise 4 by adding a regularization parameter that reduces the problem of over-fitting. We will be using Newton's Method. Data Here's the data we want to fit. # linear regression # load the data mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydHZPN2... [Read more...]

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Exercise 5.1 Improves the Linear Regression implementation done in Exercise 3 by adding a regularization parameter that reduces the problem of over-fitting. Over-fitting occurs especially when fitting a high-order polynomial, that we will try to do here. Data Here's the points we will make a model from: # linear regression mydata = read.csv("... [Read more...]

I few years ago, while I was working on kernel based density estimation on compact support distribution (like copulas) I went through a series of papers on circular distributions. By that time, I thought it was something for mathematicians working ... [Read more...]

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Exercise 4 is all about using Newton's Method to implement logistic regression on a classification problem. For all this to make sense i suggest having a look at Andrew Ng machine learning lectures on openclassroom. We start with a dataset representing 40 students who were admitted to college and 40 students who were ... [Read more...]

No need to do politics. Just take a statistical course. And I do not talk about misinterpretation of statistics, but I talk about the mathematical foundations of statistical tests. Consider the following parametric test, with a one-dimensional para... [Read more...]

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I received an email today with the following comment: I’m using ARIMA with Intervention detection and was planning to use your package to identify my initial ARIMA model for later iteration, however I found that sometimes the auto.arima function returns a model where AR/MA coefficients are not ... [Read more...]

I describe a kernel density approach to outlier detection on small datasets. In particular, my model is the set of prices for a given item that can be found online. Introduction Suppose you’re searching online for the cheapest place to … Continue reading → [Read more...]

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TOMACS (ACM Transactions on Modeling and Computer Simulation) is launching a call for paper submission. The special topic is Monte Carlo Methods in Statistics and Arnaud Doucet and myself are the special issue editors. Here are the details.: Over the last two decades Monte Carlo methods have attracted much attention ... [Read more...]

It is common to look at best time at the Marathon. Or perhaps the distribution of the top100, as done by John Myles White on his blog here (data can be found there), as the graph below, with the density of the time for the first 100 men (in blue) a... [Read more...]