Posts Tagged ‘ optimisation ’

computational methods for numerical analysis with R [book review]

October 30, 2017
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computational methods for numerical analysis with R [book review]

This is a book by James P. Howard, II, I received from CRC Press for review in CHANCE. (As usual, the customary warning applies: most of this blog post will appear later in my book review column in CHANCE.) It consists in a traditional introduction to numerical analysis with backup from R codes and packages. […]

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Le Monde puzzle [#1707]

July 27, 2017
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Le Monde puzzle [#1707]

A geometric Le Monde mathematical puzzle: Given a pizza of diameter 20cm, what is the way to cut it by two perpendicular lines through a point distant 5cm from the centre towards maximising the surface of two opposite slices?  Using the same point as the tip of the four slices, what is the way to […]

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Le Monde puzzle [#1006]

May 2, 2017
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Le Monde puzzle [#1006]

Once the pseudo-story [noise] removed, a linear programming Le Monde mathematical puzzle: For the integer linear programming problem max 2x¹+2x²+x³+…+x¹⁰ under the constraints x¹>x²+x³, x²>x³+x⁴, …, x⁹>x¹⁰+x¹, x¹⁰>x¹+x² find a solution with the maximal number of positive entries. Expressed this way, it becomes quite straightforward to solve with the help of a linear programming R […]

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Le Monde puzzle [#1002]

April 3, 2017
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Le Monde puzzle [#1002]

For once and only because it is part of this competition, a geometric Le Monde mathematical puzzle: Given both diagonals of lengths p=105 and q=116, what is the parallelogram with the largest area? and when the perimeter is furthermore constrained to be L=290? This made me jump right away to the quadrilateral page on Wikipedia, […]

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A knapsack riddle [#2]?

February 16, 2017
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A knapsack riddle [#2]?

Still about this allocation riddle of the past week, and still with my confusion about the phrasing of the puzzle, when looking at a probabilistic interpretation of the game, rather than for a given adversary’s y, the problem turns out to search for the maximum of where the Y’s are Binomial B(100,p). Given those p’s, […]

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a knapsack riddle?

February 12, 2017
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a knapsack riddle?

The [then current now past] riddle of the week is a sort of multiarmed bandits optimisation. Of sorts. Or rather a generalised knapsack problem. The question is about optimising the allocation of 100 undistinguishable units to 10 distinct boxes against a similarly endowed adversary, when the loss function is and the distribution q of the […]

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vecpack: an R package for packing stuff into vectors

September 18, 2016
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Here’s a problem I’ve had again and again: let’s say you’ve defined a statistical model with several parameters. One of them is a scalar. Another is a matrix. The third one is a vector, and so on. When fitting the model the natural thing to do is to write a likelihood function that takes as […]

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future of computational statistics

September 28, 2014
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future of computational statistics

I am currently preparing a survey paper on the present state of computational statistics, reflecting on the massive evolution of the field since my early Monte Carlo simulations on an Apple //e, which would take a few days to return a curve of approximate expected squared error losses… It seems to me that MCMC is […]

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Tuning particle MCMC algorithms

June 8, 2014
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Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased … Continue reading Tuning particle MCMC algorithms

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Tuning particle MCMC algorithms

June 8, 2014
By
Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]

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