The Baron and Sir R-----'s latest wager comprised of first placing a draught piece upon the fifth lowest of a column of twelve squares and subsequently moving it up or down by one space depending upon the outcome of a coin toss until such time as it should escape, either by moving above the topmost or below the bottommost square. In the former outcome the Baron should have had a prize of three coins and in the latter Sir R----- should have had two.

Last time we took a look at linear regression which finds the linear function that minimises the differences between its results and values at a set of points that are presumed, possibly after applying some specified transformation, to be random deviations from a straight line or, in multiple dimensions, a flat plane. The purpose was to reveal the underlying relationship between the independent variable represented by the points and the dependent variable represented by the values at them.
This time we shall see how we can approximate the function that defines the relationship between them without actually revealing what it is.

Over the course of the year my fellow students and I have been utilising our free time to explore the behaviour of cellular automata, which are mechanistic processes that crudely approximate the lives and deaths of unicellular creatures such as amoebas. Specifically, they are comprised of unending lines of boxes, some of which contain cells that are destined to live, dive and reproduce according to the occupancy of their neighbours.
Most recently we have seen how we can categorise automata by the manner in which their populations evolve from a primordial state of each box having equal chances of containing or not containing a cell, be they uniform, constant, cyclical, migratory, random or strange. It is the latter of these, which contain arrangements of cells that interact with each other in complicated fashions, that has lately consumed our attention and I shall now report upon our findings.

Several months ago we saw how we could use basis functions to interpolate between points upon arbitrary curves or surfaces to approximate the values between them. Related to that is linear regression which fits a straight line, or a flat plane, though points that have values that are assumed to be the results of a linear function with independent random errors, having means of zero and equal standard deviations, in order to reveal the underlying relationship between them. Specifically, we want to find the linear function that minimises the differences between its results and the values at those points.

Season's greetings Sir R-----! Come take a glass of mulled wine to warm your spirits on this chill winter's night!

Will you also accept a wager to warm your blood?

It gladdens my heart to hear so sir!

I propose a game that oft puts me in mind of the banquet held in the great hall upon Mount Olympus to which I was invited as the guest of honour by Zeus himself!

Over the last few months we have been looking at Bernoulli processes which are sequences of Bernoulli trails, being observations of a Bernoulli distributed random variable with a success probability of p. We have seen that the number of failures before the first success follows the geometric distribution and the number of failures before the r^th success follows the negative binomial distribution, which are the discrete analogues of the exponential and gamma distributions respectively.
This time we shall take a look at the binomial distribution which governs the number of successes out of n trials and is the discrete version of the Poisson distribution.

Recall that the Baron's most recent game involved advancing kings from the first and last ranks of a three by three chequerboard in a pawn-like manner until either he or Sir R----- reached the opposing rank or blocked all of the other's kings from moving, having the game in either eventuality.

Lately we have been looking at Bernoulli processes which are sequences of independent experiments, known as Bernoulli trials, whose successes or failures are given by observations of a Bernoulli distributed random variable. Last time we saw that the number of failures before the first success was governed by the geometric distribution which is the discrete analogue of the exponential distribution and, like it, is a memoryless waiting time distribution in the sense that the distribution for the number of failures before the next success is identical no matter how many failures have already occurred whilst we've been waiting.
This time we shall take a look at the distribution of the number of failures before a given number of successes, which is a discrete version of the gamma distribution which defines the probabilities of how long we must wait for multiple exponentially distributed events to occur.

My fellow students and I have lately been spending our spare time experimenting with cellular automata, which are simple mathematical models of single celled creatures such as amoebas, governing their survival and reproduction from one generation to the next according to the population of their neighbourhoods. In particular, we have been considering an infinite line of boxes, some of which contain living cells, together with rules that specify whether or not a box will be populated in the next generation according to its, its left hand neighbour's and its right hand neighbour's contents in the current generation.
We have found that for many such automata we can figure the contents of the boxes in any generation that evolved from a single cell directly, in a few cases from the oddness or evenness of elements in the rows of Pascal's triangle and the related trinomial triangle, and in several others from the digits in terms of sequences of binary fractions.
We have since turned our attention to the evolution of generations from multiple cells rather then one; specifically, from an initial generation in which each box has an even chance of containing a cell or not.

Last time we took a first look at Bernoulli processes which are formed from a sequence of independent experiments, known as Bernoulli trials, each of which is governed by the Bernoulli distribution with a probability p of success. Since the outcome of one trial has no effect upon the next, such processes are memoryless meaning that the number of trials that we need to perform before getting a success is independent of how many we have already performed whilst waiting for one.
We have already seen that if waiting times for memoryless events with fixed average arrival rates are continuous then they must be exponentially distributed and in this post we shall be looking at the discrete analogue.