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I have to watch why do I have to be recall why I need to be in the double domain. Of course, you could look it up in the table and you could calculate, it's not that hard mathematically. And we're discovering that these things are getting more likely because we're understanding more now about climate change. Okay, take a second and let's think about using random numbers again. So it's really only in the first move that you could use some mathematical properties of symmetry to say that this move and that move are the same.

無料 のコースのお試し 字幕 So what does Monte Carlo bring to the table? How can you turn this integer into a probability?

And we want to examine what is a good move in the five by five board. We manufacture a probability by calling double probability. And we fill out the rest of the board. You can actually get probabilities out of the standard library as well. So you might as well go to the end of the board, figure out who won.

You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. You'd have to know some facts and figures about the solar system. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you.

Who have sophisticated ways to poker star monte carlo 2019 out bridges, blocking strategies, checking strategies in whatever game or Go masters poker star monte carlo 2019 the Go game, territorial special patterns.

So here's a way to do it. I think we had an early stage trying to predict what the odds are of a straight flush in poker for a five handed stud, five card stud.

Critically, Monte Carlo is a simulation where we make heavy use of the ability to do reasonable pseudo random number generations. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board.

White https://332277.ru/2019/2019-3.html at poker star monte carlo 2019 on the board. It's int divide. So it's a very useful technique. And you're going to get some ratio, white wins over 5, how many trials? But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won.

So if I left out this, probability would always return poker star monte carlo 2019.

And that's the insight. You'd have to know some probabilities. I'll explain it now, it's worth explaining now and repeating later. We've seen us doing a money color trial on dice games, on poker. So we could stop earlier whenever this would, here you show that there's still some moves to be made, there's still some empty places. That's going to be how you evaluate that board. That's what you expect. So it's not going to be hard to scale on it. You readily get abilities to estimate all sorts of things. Instead, the character of the position will be revealed by having two idiots play from that position. And in this case I use 1. Sometimes white's going to win, sometimes black's going to win. You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. Use a small board, make sure everything is working on a small board. So we make every possible move on that five by five board, so we have essentially 25 places to move. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. So we're not going to do just plausible moves, we're going to do all moves, so if it's 11 by 11, you have to examine positions. Filling out the rest of the board doesn't matter. And indeed, when you go to write your code and hopefully I've said this already, don't use the bigger boards right off the bat. We're going to make the next 24 moves by flipping a coin. And that's a sophisticated calculation to decide at each move who has won. So it's not truly random obviously to provide a large number of trials. Because that involves essentially a Dijkstra like algorithm, we've talked about that before. You're not going to have to know anything else. Indeed, people do risk management using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. So black moves next and black moves at random on the board. Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. So we make all those moves and now, here's the unexpected finding by these people examining Go. That's the answer. This should be a review. That's the character of the hex game. And that's now going to be some assessment of that decision. All right, I have to be in the double domain because I want this to be double divide. One idiot seems to do a lot better than the other idiot. Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. You're not going to have to do a static evaluation on a leaf note where you can examine what the longest path is. Because once somebody has made a path from their two sides, they've also created a block. It's not a trivial calculation to decide who has won. So you can use it heavily in investment. The rest of the moves should be generated on the board are going to be random. Why is that not a trivial calculation? And there should be no advantage of making a move on the upper north side versus the lower south side. So you could restricted some that optimization maybe the value. And the one that wins more often intrinsically is playing from a better position. A small board would be much easier to debug, if you write the code, the board size should be a parameter. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program. So here is a wining path at the end of this game. And then by examining Dijkstra's once and only once, the big calculation, you get the result. And these large number of trials are the basis for predicting a future event. So for this position, let's say you do it 5, times. So here you have a very elementary, only a few operations to fill out the board. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. Here's our hex board, we're showing a five by five, so it's a relatively small hex board. Maybe that means implicitly this is a preferrable move. No possible moves, no examination of alpha beta, no nothing. So what about Monte Carlo and hex? I've actually informally tried that, they have wildly different guesses. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. This white path, white as one here. And you do it again. And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. So here's a five by five board. And then you can probably make an estimate that hopefully would be that very, very small likelihood that we're going to have that kind of catastrophic event. The insight is you don't need two chess grandmasters or two hex grandmasters. So it can be used to measure real world events, it can be used to predict odds making. And so there should be no advantage for a corner move over another corner move. So there's no way for the other player to somehow also make a path. And at the end of filling out the rest of the board, we know who's won the game. Given how efficient you write your algorithm and how fast your computer hardware is. And if you run enough trials on five card stud, you've discovered that a straight flush is roughly one in 70, And if you tried to ask most poker players what that number was, they would probably not be familiar with. So it's a very trivial calculation to fill out the board randomly.