DS student here, trying to grasp DeepCFR, amazing video... just wanted to point out a thing... the "P(K|Qb)" you say that you use Bayes rule and you phrase it like "P(P1 would play bet when they have a king)/P(P1 would play bet overall)"... I might be mistaking, but i don't see how you are applying Bayes... instead, I got your same result using the definition of conditional probability P(a|b) = P(a,b)/P(b)... because I get "P(KQb)/P(Qb)", now Q and b/K are independent events, so "[P(Kb)P(Q)]/[P(Q)P(b)]"... from here, applying the definition of joint probability and total probability, I get your same final probability Apart from this, thank you so much
You're correct, he's not using baye's rule/thm. It's the definition of conditional probability in Bayesian stats that he's using. It's actually fairly common for people to mix them up. Nbd
Amazing step by step example for CFR !!!
Wow amazing tutorial. Thanks ❤
This is by far the best explained CFR lesson. Thank you for doing this!
Beautifully articulated.
More videos please! Great work, you make hard things easier to visualise. I hope you get back to making RUclips videos ❤
thanks a ton for this! great video.
shouldn't folding have a utility or expected value of zero. since no money goes into the pot how can it return -1 in value?
Hi, thank you do you have any pytorch application?
DS student here, trying to grasp DeepCFR, amazing video... just wanted to point out a thing... the "P(K|Qb)" you say that you use Bayes rule and you phrase it like "P(P1 would play bet when they have a king)/P(P1 would play bet overall)"... I might be mistaking, but i don't see how you are applying Bayes... instead, I got your same result using the definition of conditional probability P(a|b) = P(a,b)/P(b)... because I get "P(KQb)/P(Qb)", now Q and b/K are independent events, so "[P(Kb)P(Q)]/[P(Q)P(b)]"... from here, applying the definition of joint probability and total probability, I get your same final probability
Apart from this, thank you so much
You're correct, he's not using baye's rule/thm. It's the definition of conditional probability in Bayesian stats that he's using. It's actually fairly common for people to mix them up. Nbd
Some people actually say that Bayes' rule/thm = definition of conditional probability. In a way, they are not completely wrong:)
You are fucking awesome Bro 🙏