See also the Markov chain applets below, my cartoon series, and some related JavaScript pages.

**NOTE:**
Most browsers no longer support Java applets properly;
see my notes about that.
Fortunately, I have recently managed to embed the
CheerpJ runner
directly into applets.
I have thus gotten
soccer
and tennismatch
and spacetag
and other applets below
working well again -- try them!

My tennismatch applet lets you interactively play tennis against a computer opponent.

My spacetag applet
(**my favourite**, including a
**cash prize**!)
is a space game, where
you fly around in a green ship and come across planets, space stations,
bad guys, etc.
See also the instructions.

My manymoons applet simulates a (randomly initialized) collection of N moons, circling each other under the influence of gravity. (Here is the corresponding source code.) Also available are a (periodic) two-body version and a (rather unstable) three-body version.

My random walk applet illustrates simple random walk, together with the famous "gambler's ruin" problem of classical probability.

My buckets applet illustrates the pouring of water into a triangular array of buckets -- sort of like Pascal's Triangle, but trickier.

My frogwalk applet simulates a 1/3,1/3,1/3 random walk on a discrete circle.

My uncunx applet illustrates that generalised quincunx device described in this paper.

My poisson applet illustrates that even if dots are placed uniformly at random, various "patterns" will seem to appear ("Poisson clumping").

A second Markov chain applet is "unif". It simulates a one-dimensional Metropolis sampler Markov chain with exponential target distribution and uniform proposal distributions. Would you trust this sampler's results?

A third Markov chain applet, "slice", simulates a one-dimensional slice sampler. See how the chain's convergence properties depend on the nature of the target distribution.

A fourth Markov chain applet, "cftp",
simulates a "coupling from the past" algorithm. See how to obtain an
*exact* sample from a distribution, using only a Markov chain for
which the distribution is stationary.

My fifth and **most useful** Markov chain applet,
"metropolis",
shows a simple random-walk Metropolis MCMC algorithm, including
an adaptive option.
Also available as a jar file, or as
a related JavaScript version.

Another Markov chain applet, "pointproc", runs a Metropolis-within-Gibbs algorithm on a spatial point process.

See also my finance-related applet "option", which uses a Monte Carlo algorithm to estimate the maximum of a stock price over a time interval.

There is also a Java applet of my Galactic Peace interactive fiction game.

Many thanks to

[contact me / Computing Page / Nonwork Page / Home Page]