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Statistical Methods in Astronomy--Lecture 3: Maximum-likelihood

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Brief Definition of Monte-Carlo by Simon Driver

Monte Carlo simulations are used to assess the errors inherent in
complex systems and are of particular use when a simple algebraic
solution or single error analysis is impossible. The simplest example
of a Monte Carlo simulation is the derivation of the probability of a
"heads" from a toss of a coin by computationally tossing a coin many
times. The success of the simulation relies on how closely the
simulation
matches reality, the resolution of the simulation and the number of
times
the simulation is run. Probably the most famous Monte Carlo simulation
is
that which derives the primordial element abundances from big-bang
nucleosynthesis based on the simulation of photon-particle interactions
during the recombination era and cooling phase. The power of the method
is in the decision as to what is and isn't simulated and the statistical
treatment of the results. If time allows I'll also summarise artificial
Neural networks, their methodology, application and pros and cons.

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