trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Bayesian reasoning is very counterintuitive. People do not employ Bayesian reasoning intuitively, find it very difficult to learn Bayesian reason
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a portable command-line driven interactive data and function plotting utility for UNIX, IBM OS/2, MS Windows, DOS, Macintosh, VMS, Atari and many other platforms.
Following the success of last season, my thoughts obviously turned to whether this can be repeated. Or more accurately, what can be expected from the algorithm betting model in future seasons both in terms of return and the variability of those returns. “Monte Carlo” is the name given to simulations which make use of computer generated random numbers to identify the range of possible outputs a model may generate in the ‘real world’. Each random number generates an input from a user defined probability distribution which is run through the user’s model to produce a simulated outcome on each run. Run this simulation thousands of times and you generate a probability distribution for the output of your model. Assigning a probability distribution to football match results