Suppose we flip our coin twice.

I'm going to flip a coin.

is being switched by a coin flip?

There's a flip side to that coin.

You and me! Coin flip is sacred.

Take a coin flip. There is a true probability p for the coin.

Suppose I flip a coin, and the coin comes up tails or heads.

Thrun Now let's flip the coin 4 times, and let's call Xi the result of the i th coin flip.

The x's that we've used here because the random coin flip doesn't understand what the order of the coin flip is.

It's like a coin flip it doesn't count, really.

you wanna flip a coin to see who leads?

And then I'm going to flip that coin, I'm going to flip it five times.

But this is the flip side of his coin, Harry.

We flip the coin a 1000 times which is a lot.

We flip a coin, comes up heads, so we connect them.

And you can run that simulation. You can flip a coin.

Anyway, let's say I'm going to now flip a coin twice.

Let's just flip it once, so with one flip of the coin, what's the probability of getting heads?

We now flip this coin again (the same coin), and we see heads again for a second time.

The first coin flip might also come up tails, in which case I pick a very different coin.

You can either take a risky loss so I'll flip a coin.

Every time you flip a coin, you double the number of sequences.

You randomly choose one coin from the bag and flip it 4 times

I flip a fair coin of getting exactly 7 out of 8 heads?

In assignment 1, I literally want you to flip a coin 1000 times.

At 80 20, it's a coin flip whether you accept that or not.

Now, we go in that courtroom, we might as well flip a coin.

Let me begin. What is the expected mean outcome when you flip this coin?

Let me now flip that coin once, and I want you to tell me, what's the probability that this coin which could be 50 chance fair coin and 50 chance a loaded coin.

The way I word this is by conditional probability, probability of the second coin flip coming up heads provided that or given that X1, the first coin flip, was heads, is 0.9.

From a single coin flip, the maximum likelihood estimator will always assume that all future coin flips come from the same way.

Technically speaking, this thing over here is called independence, which means nothing else, but that the outcome of the second coin flip didn't really depend on the outcome of the first coin flip.

We now pick a coin at random with 0.5 chance, and we don't quite know which coin we've picked, but we do flip this coin, and we see heads.

Now, I'm going to draw one coin from this bag and I'm going to flip it repeatedly and each time I flip it,

What you've done is for the coin flip example, you've actually computed confidence intervals yourself.

What's the probability of getting let's say I were to flip a coin seven times.

In coin flipping, we flip n coins. In elections, we ask n randomly chosen people.

You learned about probability of an event, such as the outcome of a coin flip.

A loaded coin is a coin, that if you flip it, might have a non 0.5 chance of coming up heads or tails.

So let's say I want to figure out the probability I'm going to flip a coin eight times and it's a fair coin.

If you and I flip a coin and it comes up heads, we would not say obviously this coin always comes up heads.

In one room there is a man who flips his switch according to a coin flip.

So there's 2 to the eighth possible outcomes when you flip a fair coin eight times.

Chance, the flip of the coin, doesn't get the choice of one direction or the other.

And then whichever coin I get I'm going to flip it five times in a row.