Posts Tagged ‘ Probability and Statistics ’

Why not statistics

April 9, 2015
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Jordan Ellenberg’s parents were both statisticians. In his interview with Strongly Connected Components Jordan explains why he went into mathematics rather than statistics. I tried. I tried to learn some statistics actually when I was younger and it’s a beautiful subject. But at the time I think I found the shakiness of the philosophical underpinnings […]

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Bayes factors vs p-values

March 31, 2015
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Bayesian analysis and Frequentist analysis often lead to the same conclusions by different routes. But sometimes the two forms of analysis lead to starkly different conclusions. The following illustration of this difference comes from a talk by Luis Pericci last week. He attributes the example to “Bernardo (2010)” though I have not been able to find the exact […]

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Fitting a triangular distribution

March 24, 2015
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Sometimes you only need a rough fit to some data and a triangular distribution will do. As the name implies, this is a distribution whose density function graph is a triangle. The triangle is determined by its base, running between points a and b, and a point c somewhere in between where the altitude intersects the base. […]

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Finding the best dose

February 24, 2015
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In a dose-finding clinical trial, you have a small number of doses to test, and you hope find the one with the best response. Here “best” may mean most effective, least toxic, closest to a target toxicity, some combination of criteria, etc. Since your goal is to find the best dose, it seems natural to compare dose-finding […]

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Miscellaneous math resources

February 4, 2015
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Every Wednesday I’ve been pointing out various resources on my web site. So far they’ve all been web pages, but the following are all PDF files. Probability and statistics: How to test a random number generator Predictive probabilities for normal outcomes Predictive probability interim analysis Relating two definitions of expectation Illustrating the error in the […]

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Probability approximations

January 28, 2015
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This week’s resource post lists notes on probability approximations. Do we even need probability approximations anymore? They’re not as necessary for numerical computation as they once were, but they remain vital for understanding the behavior of probability distributions and for theoretical calculations. Textbooks often leave out details such as quantifying the error when discussion approximations. The […]

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More data, less accuracy

January 27, 2015
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Statistical methods should do better with more data. That’s essentially what the technical term “consistency” means. But with improper numerical techniques, the the numerical error can increase with more data, overshadowing the decreasing statistical error. There are three ways Bayesian posterior probability calculations can degrade with more data: Polynomial approximation Missing the spike Underflow Elementary numerical integration algorithms, […]

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Random probability tweets

December 3, 2014
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-+*For the next few weeks, I’ve scheduled @ProbFact tweets to come out at random times. They will follow a Poisson distribution with an average of two per day. (Times are truncated to multiples of 5 minutes because my scheduling software requires that.)  

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First two impressions of statistics

November 25, 2014
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-+*When I was a postdoc I asked a statistician a few questions and he gave me an overview of his subject. (My area was PDEs; I knew nothing about statistics.) I remember two things that he said. A big part of being a statistician is knowing what to do when your assumptions aren’t met, because […]

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Blue Bonnet Bayes

October 28, 2014
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Blue Bonnet Bayes

-+*Blue Bonnet™ used to run commercials with the jingle “Everything’s better with Blue Bonnet on it.” Maybe they still do. Perhaps in reaction to knee-jerk antipathy toward Bayesian methods, some statisticians have adopted knee-jerk enthusiasm for Bayesian methods. Everything’s better with Bayesian analysis on it. Bayes makes it better, like a little dab of margarine […]

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