thank

25:40 You mention that the bounds for x should be 0 to 3/2. But doesn't that only apply to the strip of area where y = 3/2? When y is from 0 to 3/2 the bounds would be x = 0 to x = y. And when y is from 3/2 to 3, the bounds would be x = 0 to x = 3 - y. I thought that was how u would do it. Why does what u did work?

so helpful! and so clear you saved me for test day!

Ma'am I did not get it why did we add those 2 probs from drawing black ball in draw 2? My doubt is - isn't the two things (getting the white ball in first draw or getting a black ball in first draw) different? Both the cases are different right? Why addition then? Btw, loving your maths series. Thanks a lot!

was perfect would you please explain more statistics

You are the best

The code used to get the predictive distribution with the number of simulations inside rbeta and rbinom does the same as the one with the for loop at the predictive checking part, this second option being less efficient. Nevertheless, I think it was better to show it that way from a didactic point of view.

WHY IS THIS SO UNDERRATEDDDD?😭😭THANK YOU SO MUCH ❤

finally someone explains it in a way that makes sense! thanks so much!

Very very helpful

thank you for your insightful videos

How to hit 5000.69 subscribers

This is cool man, I am doing genomics research currently and I have to implement multivariate generalized Bayesian linear regression to find how genetic factors simultaneously influence traits. Your research, though in a different field, is very helpful as a reference and interesting too!!

I'm totally failing this ;-;

why were we sampling from the prior distribution for posterior prediction? Seems like an obvious error.

Ma'am, can I get the lecture note pdf?

Great explanation. Has anyone ever thought of using these ideas for a language model? I could have continuous learning built in, due to the Bayesian Approach?

What book do you use?

Please which textbook are you referring to in the video?

what are the prerequisites for this course

Thank you for sharing those videos!

Thank you for your video! Your explaination is very clear and I've learned much from it. Please make more videos.

Hi Prof, i am not sure why " has the probability changed between 1992 and 1993" got interpreted mathematically into Pr(P1 < P2). It could also have been interpreted as Pr(P2 < P1) which would have a different result. The data samples are independent from each other (ie. data from 1992 is independent from 1993 and vice versa) hence, in my mind, the ordering should not matter.

Thannk you, excellent video!

but what the case for the difference between two dependent uniform random variables ? would you kindly make a case vedio for that

ok

excelente ejemplo/ejercicio! muchas gracias

谢谢老师 确实帮助比较大

Great video!

Okay.... Okay.... Okay

Thanks dia for reaching out to our understanding. Bit is there anyway I can access you slides in pdf format?

How do I do it for three iid uniform random variables?

which slides are you using? I can not find it in your github repo, the one with the restaurant one

Will ruin folks

ok? ok? ok? ok? ok?

When cdf is nondecreasing function is it right continuous??

Yes, Chinese explaining math. We can be 100% sure that it is correct.

A random variable itself is a set of values. These values can be interpreted as data points. E[X] is the weighted average (= the outcome that we most likely expect). Variance Var[X] is the degree of spread in the set of data points around E[X]. It shows the amount of variance among the data points. The larger the variance, the „fatter“ the distribution (= the graph is spread further from the middle point E[X]). Covariance Cov(X,Y) inspects how the values of two random variables X,Y correspond to each other. If Cov(X,Y) is large, then X and Y have correlating high values (= the values are high at the same points). For example: Studying more correlates to higher grades. But if Cov(X,Y) is negative, it shows that the high values of X correspond with the low values Y. For example: If it rains a lot, less people go outside.

There is a simple derivation for the expectation of X, s.t E[X] = n*p A binomial distribution Bin(n,p) is just the sum of n INDEPENDENT Bernoulli Distributions Y with probability p. As we know, E[Y] = p. Thus E[X] = n * E[Y] = n*p

Thank you so much you just made this so much easier ❤

The indicator function Z for an event A has ONLY two values. If x € A => Z(x) = 1 If x !€ A => Z(x) = 0 Since the sample space is just a partition of A and its complement A^c, we have: sample space = A u A^c Thus, E[Z] = 1*P[A] + 0*P[A^c] = 1*P[A] + 0*(1-P[A]) = P[A]

P[X = „Zero ex-girlfriends for CS majors“] = 1.0

{X <= b} = {X <= a} u {a < X <= b} (Union of disjunctive sets, use sigma-additivity of P): P[X <= b] = P[X <= a] + P[a < X <= b] ==> P[a < X <= b] = P[X <= b] - P[ X <= a] = F(b) - F(a)

Great tutorial. Please post more videos about DP

This course has solved so many questions I have for probability and I am feeling so much more confident about the coming up final! Thank you so much for your excellent explanation and for sharing those videos!

Thank you so much it helps a lot!

is there a text book this class is based upon ?

Thank u for explaining in detail!

Thanks

谢谢胡老师开放这门课程，对我帮助很大