# Posts Tagged ‘ optimisation ’

## computational methods for numerical analysis with R [book review]

October 30, 2017
By

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. […]

## Le Monde puzzle [#1707]

July 27, 2017
By

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 […]

## Le Monde puzzle [#1006]

May 2, 2017
By

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 […]

## Le Monde puzzle [#1002]

April 3, 2017
By

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, […]

## A knapsack riddle [#2]?

February 16, 2017
By

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, […]

## a knapsack riddle?

February 12, 2017
By

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 […]

## vecpack: an R package for packing stuff into vectors

September 18, 2016
By

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 […]

## future of computational statistics

September 28, 2014
By

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 […]

## Tuning particle MCMC algorithms

June 8, 2014
By

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

## Tuning particle MCMC algorithms

June 8, 2014
By

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 […]