21. Bayesian Statistical Inference I

MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010
View the complete course: ocw.mit.edu/6-041F10
Instructor: John Tsitsiklis
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu

[@dania_884]

his teaching is rigorous, precise, strict and concise, to the point, helps me clarifying the cloudy understanding in statistics.

[@EndureTemptation]

This is a very fitting explanation for me, hwo has studied statistics, but some time ago. This really clarifies things.

[@philippe177]

Excellent. It is so to the point and brilliant. It is like a diamond!

[@umbrellageeks9421]

Amazing class ! This had helped me a lot in my studies in statistics and probability for Artificial Intelligence. Thank you!!

[@mrnettek]

Amazing! Thanks MIT and Professor.

[@MegaBeautysoul]

Excellent professor

[@salaheamean]

this prof. is just perfect. I wish he taught me...!!

[@oglala_lakota2o]

i believe this particular professor has reached a level of understanding on bayesian inference that is referred to as hyper-sanity, this was a beautiful lecture, similar to classical music like panagini

[@Rainstorm121]

Thanks Sir. Couldn't get well the big diff between posterior and prior distributions.

[@Gruemoth]

a very clear, no wishy washy lecture on Bayesian Statistical Inference

[@alinapol]

great lecture

[@zyzzyva57]

Excellent presentation...Start watching 10 minutes in
...Bayesian starts 18 minute in

[@Bea_remembrance]

Nice video thanks will use information to finish my book

[@markchambers9378]

Seriously good lecture. Very thoughtful presentation.

[@torincody8378]

sorry to be so off topic but does anyone know a way to log back into an Instagram account??
I was dumb lost my account password. I love any tricks you can give me.

[@alessandronoel4289]

@Torin Cody Instablaster ;)

[@torincody8378]

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I see it takes quite some time so I will reply here later with my results.

[@torincody8378]

@Alessandro Noel It worked and I actually got access to my account again. I'm so happy!
Thanks so much, you really help me out !

[@alessandronoel4289]

@Torin Cody glad I could help :)

[@melodyh5993]

couldn't be better.

[@anyonetherealready]

he is a legend

[@nabilakdim2767]

Hi, Thank you for these nice videos.
One small note : at 27:40, I think that to justify the use of uniform distribution for θ, one should use entropy, so given that uniform distribution (discrete r.v)/Guassian (continious r.v with a given 1st and 2nd moments) has the highest entropy, i.e the highest degree of randomness, if we estimate/detect θ assuming those distributions, the solution would be the best (in randomness sense, so lower probability of error maybe ?) also for other distributions based θ, given that those other distributions have lower degree of randomness.
Cheers
Nabil

[@shubham2488]

Agreed.

[@ayubaalim2201]

very helpful

[@lemyul]

thanks

[@hanxiaoliu4941]

Thx Professor

[@cemsavasaydn7053]

5:00 I would definitely use Support Vector Machine

[@MrSinalta]

Not sure as SVM want to separate data in 2 separates cluster and this problem is not exactly of this type

[@Palikpalik9964]

Bayesian, yes bayesian..

[@pramu_dithawickrama_tunga1672]

Statistics is nothing else
1.deviation
2.mean
Equals,
Understanding primes
Equals,
The clay institute statement about riemann hypothesis:
1.The PNT determines the average distribution of the primes.
2.The riemann hypothesis tells us about the deviation from the average. Average mens statistical mean or 0
Stochastic means 0
Statistis means 0.
Order means 1.

[@pramu_dithawickrama_tunga1672]

Whole mathemtics is applied.

[@tobiasfan5407]

His sniffling is making me sniffle

[@aylinakkus7753]

i am big Aylin

[@jamespaz4333]

Excellent accent

[@mazensayed3250]

opa el machine learning

[@y031962]

Too much talk and not enough example. He doesn't not go through one single basic example to help understanding the subject. He just repeats what is said on intorduction to probablity by Dimitri Berteskas. Just read the book is just as good as watching this video. Unless u know the subject very well, I would not recommend watching the video

[@PewPew4QQ]

This is MIT, not Univ of Podunktown.

[@c_na_man]

y031962 he is the co-author of that book.

[@penny6724]

+y031962 Have you ever read the book sly?

[@jayquelin]

dude ur just a bad who didnt study his probability theory

[@christianjimenez1877]

Well. That's the MIT system. Moreover, you may say there are not questions. This is because it is a Lecture.
Recitations and Tutorials complete the educational structure. However, this Lecture is clear and powerful.
Professor Tsitsiklis has 45 minutes to communicate his ideas and does it in an excellent manner.