bigjeff5

This is exactly right. To put it more succinctly: Memory corruption is a known vector for exploitation, therefore any bug that potentially leads to memory corruption also has the potential to be a security vulnerability. Thus memory corruption should be treated with similar care as a security vulnerability.

The reason Person A in scenario 2 has the intuition that Person B is very wrong is because there are dozens, if not hundreds of examples where people claimed no vulnerabilities and were proven wrong. Usually spectacularly so, and often nearly immediately. Consider the fact that the most robust software developed by the most wealthy and highly motivated companies in the world, who employ vast teams of talented software engineers, have monthly patch schedules to fix their constant stream vulnerabilities, and I think it's pretty easy to immediately discount a... (read more)

That was four years ago, but I'm pretty sure I was using hyperbole. Pros don't bluff often, and when they do they are only expecting to break even, but I doubt it's as low as 2% (the bluff will fail half the time).

I'd also put in a caveat that the best hand wins among hands that make it all the way to the river. There are plenty of times where a horrible hand like a 6 2, which is an instant fold if you respect the skills of your fellow players, ends up hitting a straight by the river and being the best hand but obviously didn't win. Certainly more often th... (read more)

Pretty much.

At this point you have to ask what you mean by "theory" and "learning".

The original method of learning was "those that did it right didn't die" - i.e. natural selection. Those that didn't die have a pattern of behavior (thanks to a random mutation) that didn't exist in previous generations, which makes them more successful gene spreaders, which passes that information on to future generations.

There is nothing in there that requires one to ask any questions at all. However, considering that there is information gained based ... (read more)

I see that now, it took a LOT for me to get it for some reason.

Wow.

I've seen that same explanation at least five times and it didn't click until just now. You can't distinguish between the two on tuesday, so you can only count it once for the pair.

Which means the article I said was wrong was absolutely right, and if you were told that, say one boy was born on January 17th, the chances of both being born on the same day are 1-(364/365)^2 (ignoring leap years), which gives a final probability of roughly 49.46% that both are boys.

Thanks for your patience!

ETA: I also think I see where I'm going wrong with the terminology - sampling vs not sampling, but I'm not 100% there yet.

How can that be? There is a 1/7 chance that one of the two is born on Tuesday, and there is a 1/7 chance that the other is born on Tuesday. 1/7 + 1/7 is 2/7.

There is also a 1/49 chance that both are born on tuesday, but how does that subtract from the other two numbers? It doesn't change the probability that either of them are born on Tuesday, and both of those probabilities add.

This statement leads me to believe you are still confused. Do you agree that if I know a family has two kids, I knock on the door and a boy answers and says "I was born on a Tuesday," that the probability of the second kid being a girl is 1/2? And in this case, Tuesday is irrelevant? (This the wikipedia called "sampling")

I agree with this.

Do you agree that if, instead, the parents give you the information "one of my two kids is a boy born on a Tuesday", that this is a different sort of information, information about the s

The answer I'm supporting is based on flat priors, not sampling. I'm saying there are two possible Boy/Boy combinations, not one, and therefore it takes up half the probability space, not 1/3.

Sampling to the "Boy on Tuesday" problem gives roughly 48% (as per the original article), not 50%.

We are simply told that the man has a boy who was born on tuesday. We aren't told how he chose that boy, whether he's older or younger, etc. Therefore we have four possibilites, like I outlined above.

Is my analysis that the possibilities are Boy (Tu) /Girl, G... (read more)

For the record, I'm sure this is frustrating as all getout for you, but this whole argument has really clarified things for me, even though I still think I'm right about which question we are answering.

Many of my arguments in previous posts are wrong (or at least incomplete and a bit naive), and it didn't click until the last post or two.

Like I said, I still think I'm right, but not because my prior analysis was any good. The 1/3 case was a major hole in my reasoning. I'm happily waiting to see if you're going to destroy my latest analysis, but I think it is pretty solid.

Yes, and we are dealing with the second question here.

Is that not what I said before?

We don't have 1000 families with two children, from which we've selected all families that have at least one boy (which gives 1/3 probability). We have one family with two children. Then we are told one of the children is a boy, and given zero other information. The probability that the second is a boy is 1/2, so the probability that both are boys is 1/2.

The possible options for the "Boy born on Tuesday" are not Boy/Girl, Girl/Boy, Boy/Boy. That would be th... (read more)

Yeah, probably the biggest thing I don't like about this particular question is that the answer depends entirely upon unstated assumptions, but at the same time it clearly illustrates how important it is to be specific.

The relevant quote from the Wiki:

The paradox arises because the second assumption is somewhat artificial, and when describing the problem in an actual setting things get a bit sticky. Just how do we know that "at least" one is a boy? One description of the problem states that we look into a window, see only one child and it is a boy. This sounds like the same assumption. However, this one is equivalent to "sampling" the distribution (i.e. removing one child from the urn, ascertaining that it is a boy, then replacing). Let's call the s

Re-read it.

http://en.wikipedia.org/wiki/Boy_or_Girl_paradox

I know it's not the be all end all, but it's generally reliable on these types of questions, and it gives P = 1/2, so I'm not the one disagreeing with the standard result here.

Do the math yourself, it's pretty clear.

Edit: Reading closer, I should say that both answers are right, and the probability can be either 1/2 or 1/3 depending on your assumptions. However, the problem as stated falls best to me in the 1/2 set of assumptions. You are told one child is a boy and given no other information, so the only probability left for the second child is a 50% chance for boy.

How is it different? In both cases I have two independent coin flips that have absolutely no relation to each other. How does knowing which of the two came up heads make any difference at all for the probability of the other coin?

If it was the first coin that came up heads, TT and TH are off the table and only HH and HT are possible. If the second coin came up heads then HT and TT would be off the table and only TH and HH are possible.

The total probability mass of some combination of T and H (either HT or TH) starts at 50% for both flips combined. On... (read more)

No, it's the exact same question, only the labels are different.

The probability that any one child is boy is 50%. We have been told that one child is a boy, which only leaves two options - HH and HT. If TH were still available, then so would TT be available because the next flip could be revealed to be tails.

Here's the probability in bayesian:

P(BoyBoy) = 0.25 P(Boy) = 0.5 P(Boy|BoyBoy) = 1

P(BoyBoy|Boy) = P(Boy|BoyBoy)*P(BoyBoy)/P(Boy)

P(BoyBoy|Boy)= (1*0.25) / 0.5 = 0.25 / 0.5 = 0.5

P(BoyBoy|Boy) = 0.5

It's exactly the same as the coin flip, because the pro... (read more)

Lets add a time delay to hopefully finally illustrate the point that one coin toss does not inform the other coin toss.

I have two coins. I flip the first one, and it comes up heads. Now I flip the second coin. What are the odds it will come up heads?

The only relevant information is that one of the children is a boy. There is still a 50% chance the second child is a boy and a 50% chance that the second child is a girl. Since you already know that one of the children is a boy, the posterior probability that they are both boys is 50%.

Rephrase it this way:

I have flipped two coins. One of the coins came up heads. What is the probability that both are heads?

Now, to see why Tuesday is irrelevant, I'll re-state it thusly:

I have flipped two coins. One I flipped on a Tuesday and it came up heads. What is ... (read more)