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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. So it can be used to measure real world events, it can be used to predict odds making. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you. Use a small board, make sure everything is working on a small board. So you can use it heavily in investment. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program. But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. So it's not truly random obviously to provide a large number of trials. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. So there's no way for the other player to somehow also make a path. You can actually get probabilities out of the standard library as well. 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. And we fill out the rest of the board. White moves at random on the board. So here is a wining path at the end of this game. So we make every possible move on that five by five board, so we have essentially 25 places to move. 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. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. Why is that not a trivial calculation? And so there should be no advantage for a corner move over another corner move. So black moves next and black moves at random on the board. And you do it again. We've seen us doing a money color trial on dice games, on poker. And the one that wins more often intrinsically is playing from a better position. We manufacture a probability by calling double probability. Maybe that means implicitly this is a preferrable move. 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. Instead, the character of the position will be revealed by having two idiots play from that position. So it's not going to be hard to scale on it. And in this case I use 1. So you might as well go to the end of the board, figure out who won. It's not a trivial calculation to decide who has won. Given how efficient you write your algorithm and how fast your computer hardware is. And that's the insight. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. You readily get abilities to estimate all sorts of things. So it's a very useful technique. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. Here's our hex board, we're showing a five by five, so it's a relatively small hex 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. 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. And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. So for this position, let's say you do it 5, times. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. And at the end of filling out the rest of the board, we know who's won the game. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. How can you turn this integer into a probability? Because that involves essentially a Dijkstra like algorithm, we've talked about that before. We're going to make the next 24 moves by flipping a coin. You'd have to know some facts and figures about the solar system. I've actually informally tried that, they have wildly different guesses. Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. Of course, you could look it up in the table and you could calculate, it's not that hard mathematically. And we want to examine what is a good move in the five by five board. And you're going to get some ratio, white wins over 5,, how many trials? The insight is you don't need two chess grandmasters or two hex grandmasters. So here's a five by five board. This white path, white as one here. You're not going to have to know anything else. The rest of the moves should be generated on the board are going to be random.

無料 のコースのお試し 字幕 So what does Monte Carlo bring to the table? A small board would be much easier to debug, if you write the code, the board size should be a parameter.

So here's a way to do it. That's going to be how you evaluate that board. Critically, Monte Carlo is a simulation where we make heavy use of the ability to 勝ち 方 ウィリアムヒル reasonable poker star monte carlo 2019 random number generations.

That's what you expect. That's https://1-veda.ru/2019/poker-monte-carlo-2019-episode-4.html answer. I'll explain it now, it's worth explaining now and repeating later.

Indeed, people do risk poker star monte carlo 2019 using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. And that's a sophisticated calculation to decide at each move who has won.

That's the character of the hex game. And we're discovering poker star monte carlo 2019 these things are getting more likely because we're understanding more now about climate change. This should be a review. So it's a very trivial calculation to fill out the board randomly. Sometimes white's going to win, sometimes black's going to win.

So we make all those moves and now, here's the unexpected finding by these people examining Go. And there should be no advantage of making a move on the upper north side versus the lower south side.

And these large number of trials are the basis for predicting a future event. Okay, take a poker star monte carlo 2019 and let's think about using random numbers again.

Filling out the rest of the board doesn't matter. One idiot seems to do a lot better than the other idiot. 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. You'd have please click for source know some probabilities.

No possible moves, no examination of alpha beta, no nothing. 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.

It's int divide. So what about Monte Carlo and hex? Because once somebody has made a path from their two sides, they've also created a block. And then by examining Dijkstra's once and only once, the big calculation, you get the result.

So if I left out this, probability would always return 0.

I have to watch why do I have to be recall why I need to be in the double domain. Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. 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. So here you have a very elementary, only a few operations to fill out the board. Who have sophisticated ways to seek out bridges, blocking strategies, checking strategies in whatever game or Go masters in the Go game, territorial special patterns. So you could restricted some that optimization maybe the value.