monte

There has yet to be a combination of the Neural Network and Monte Carlo methods, but they're quite complimentary.

If you are doing a monte carlo simulation, then you can get good results while still starting runs from predictable seeds.

It sounds like a good solution might be a Markov Chain Monte Carlo method.

Unfortunately, this requires either crazy stochastic math, or multiple computations (monte carlo methods).

The implication of this randomizing / Monte Carlo integration is that it typically takes many ray paths to get the color for a single pixel.

It's a Monte Carlo integration algorithm for solving the nasty integrals.

But it converges faster than most other Monte Carlo techniques.

I liked the ability to use various random variables for a Monte Carlo simulation.

Basically what we want to solve is a path integral (an integral over all paths), we do this using a technique called Monte Carlo integration (which basically means we use randomness).

I used a basic Monte Carlo technique to help buy a new house.

I'm a big fan of Monte Carlo simulations, and highly encourage their use.

It's the same reaction I have to the Monte Hall problem: I don't have to think or be clever to get the right answer.

One potential source of confusion is that "Monte Carlo method" and "Monte Carlo algorithm" are not the same.

Not to be pedantic, but it's "Monte Carlo", not "Money Carlo".

Then read this in the summary: "When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move."

Monte Carlo Tree Search ( Random playout ) is currently the best computer strategy for evaluating a Go position.