6. Monte Carlo Simulation

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses the Monte Carlo simulation, Roulette
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu

[@splashd]

The sign of a good teacher--I landed here by accident, stayed for the entire lecture, and understood all of it...

[@leixun]

*My takeaways:* 1. History of Monte Carlo Simulation 0:56 2. Monte Carlo Simulation 3:23 - Example1: coins 6:03 - Variance 10:00 - Example2: Roulette 11:00 3. Law of large numbers 18:40 4. Misunderstanding on the law of large numbers: Gambler's fallacy 19:48 5. Regression to the mean 22:42 6. Quantifying variation in data: variance and standard deviation 30:14 - Always think about standard deviation in the context of mean 35:10 7. Confidence level and intervals 36:00 8. Empirical rule for computing confidence intervals 39:27 9. Assumptions underlying empirical rule 43:40 - mean estimation error is 0 - Normal distribution 10. Probability density function 46:25

[@dr.mohamedaitnouh4501]

thank you Mr. Lei

[@leixun]

Dr. Mohamed Ait Nouh you’re welcome :)

[@pajeetsingh]

Thanks Mr. Lel

[@leixun]

Pajeet Singh you’re welcome

[@imrs07]

Thank you Mr. Lei

[@kepstein8888]

This is a true teacher. He actually explains the concepts instead of just scribbling equations on the board.

[@cly5570]

Couldn't agree more. I am hooked.

[@lidarman2]

Why MIT is a top school. I love that MIT allows anyone to watch these for free.

[@IonidisIX]

COULD NOT AGREE MORE!!! He is truly amazing. Suddenly the Stats I did on a Data Science Coursera course start to make sense. A couple of more lectures by him and I will have everything sorted out in my mind... My God. Some lecturers just Got it and some just Don't.

[@benphua]

I wonder how much time and effort was made to ensure every word was meaningful and carefully stated (just been through a course with a lecturer who knew his stuff but mostly winged it which was one of the biggest wastes of my time). I also noticed not a single 'um' or 'uh' which is amazing.

[@cbarlow3]

@@benphua Well, I noticed four "ums" or "uhs" in second 0:35 to 0:45 alone, but I agree the lecture is very clear.

[@hamidrajabi8775]

I've never met him, but he taught me python years ago. we should be grateful for such giving human beings.

[@mikebernard8535]

For those looking for some visuals of how a Monte Carlo simulation works, see the second half or so of lecture 7 on Confidence Intervals.

[@francissydnor7891]

MVP

[@przemysawniedziela4631]

Thanks a lot, that was what I was looking for!

[@bharathsf]

Which playlist??

[@pepegallardo4060]

Watching Prof. Guttah teaching is a joy. A true inspiration for those of us who also like teaching and want to do better

[@kenerwin5198]

This guy is such a fantastic teacher. I would love to have him in person, thanks again for uploading the video!

[@zZE94]

Have him for ... breakfast?

[@antoniomoraes1741]

@@zZE94 Ken really sounded weird ahahahha

[@DaviSouza-kq7xz]

He prolly would love have you in person too, for sure.

[@dennis1836]

At the university where I studied all teachers were also fantastic teachers until the exam. Afterwards they were all a**h****.

[@mdcamp00]

Some of the best explanations of statistics I’ve heard. Does a great job of breaking down concepts.

[@27eharkness]

Not what I was looking for, but couldn't help but watch the entire video. Well done sir.

[@vydanzthechamp]

same

[@SuperFreelibya]

The same!

[@danielschaben]

I love random walks through youtube

[@GaoyuanFanboy123]

wanted to know what a monte carlo simulation is but I guess ill revise some stats intuition ¯\_(ツ)_/¯

[@3ndr3wmusic56]

@@GaoyuanFanboy123 hahaah same xD

[@sitrakaforler8696]

00:00 Monte Carlo simulation is a method of estimating unknown quantities using inferential statistics. 06:48 Variance affects confidence in probability predictions 13:09 Law of large numbers: Expected return of fair roulette wheel is 0 over infinite spins 19:23 Understanding the Gambler's Fallacy and Regression to the Mean 25:16 Regression to the mean is a statistical phenomenon where extreme events tend to move towards the average with more samples. 31:11 Understanding variance and standard deviation for computing confidence intervals. 37:37 Understanding confidence intervals and the empirical rule 44:04 Probability distributions can be discrete or continuous, and are described by probability density functions. Crafted by Merlin AI.

[@iPergjakshem]

I came here for the Monte Carlo simulation but got unexpectedly thus far the best explanation for simple concepts like Variance or Standard Deviation

[@durgeshkinnerkar2826]

Brilliant lecture. I can binge watch Professor John Guttag's lectures. Amazing.

[@aayushkhanal5564]

What a beautiful way to explain a concept. Starts with something so simple and gradually builds up to the more complex part, also delivers the lecture in a way that even a tiny bit of boredom can't creep in.

[@habeebyusuf7037]

this man right here is a true teacher, understands the subject topic deeply and speaks passionately

[@ractheworld]

Isn't he the most adorable teacher ever? Great job walking your audience through the material!

[@jerryreed2050]

An instructor of the highest caliber; clear explanations, projects a seemingly universal likeable and fair personality, low intensity approach. Good hire MIT!

[@yusuffarah351]

Great teaching style. Small number of teachers can teach such concise and clarify. I learn a lot from the great educators.

[@keyaamarsee9631]

Thank you for this great lecture. You explain it so well. I was looking for Monte Carlo Simulation but ended up watching the whole video.

[@fabbiotec]

WANTED MORE ABOUT MONTE CARLO, but he is such an amazing teacher that I got stuck anyways!!!!

[@paulmctaggart6947]

Had this same lecture in PSYCH Stats class at CofC. Learned a lot and this was fun to watch again

[@JebBradwell]

I love professors who make mistakes and make corrections accepting help from students.

[@CodeJeffo]

Wonderful professor. So casual but I believe what the students learn will stick with them forever.

[@satoshinakamoto171]

such respect for these fantastic teachers

[@robertkelleher1850]

For those that may be confused, he misspoke at 23:36 "taller than average" should have been "taller than the parents". In the case that parents are shorter than average, it is expected that their children will be taller than them, not taller than average.

[@alperensayar9679]

Hayatımdaki en iyi üniversite dersiydi.Thanks Prof J. Guttag

[@paulorufalco]

12:47 "win some lose some, it's all the same to me" Lemmy

[@longn.8804]

I love the sense of humour of the instructor. A great lecture indeed!

[@satria5403]

Thank you for share this amazing video

[@georgejetson9801]

I love these old school professors. They are true masters.

[@annakh9543]

he is so funny, i wish i had such professors

[@GbUnLimiteD]

26:53 Great answer to make the difference between gambler's fallacy and regression to the mean clear!

[@d.v.faller9251]

Excellent lecture. Prof. Guttag is a great teacher. Thank you. Every course or lecture I have watched in this MIT Open Courseware has been superb. Thank you to the teachers and to MIT for posting.

[@nikolavalizadeh133]

Unfortunately, during my studies at Bachelor and Master, I never had such great real professor. Thanks so much for sharing such great video.

[@ridhikakhanna6383]

After watching this lecture, I wish I was smart enough to get into such elite schools and be taught by such passionate teachers. Respect!

[@dxhunzai]

But you have access to MIT open courseware

[@papasmurf9146]

Excellent presentation. Don't know why KZbin presented the option of the video, but watched until the end. Very gifted professor. The only thing that I can think to improve it is to repeat the question from the audience so that the question is picked up on the recording.

[@alvinsihombing7554]

Thank you Prof. Guttag & MIT.

[@kasra545]

Finally understood what statistics is about after 10 years of endeavour! Thanks so much!

[@howardlam6181]

Trying applying it to obtain Lebsegue Integral. See, you probably have understood nothing.

[@harshabhogle1020]

Kasra Keshavarz your face shows how stupid you are

[@AbhishekSingh-pp1ks]

Howard Lam. It is “Lebesgue”

[@OmarMagdyNofal]

Actually you are an amazing demonstrator

[@gustavogodoy5823]

Wow... fantastic lecture by Prof. Guttag... Thank you and congratulations.

[@androsram641]

Thanks you for being a great teacher. I really needed some background on Montecarlo.

[@anthonycicero6102]

great video, such a clean delivery of the concepts. well done

[@dennisangelomarasigan2431]

Great lecture. The concepts were explained clearly. I understood them very well. Thank you!

[@melgibsonsmyhero]

Thanks for sharing this video. Concepts very well explained and accessible. Thank you.

[@markimark8445]

Very interesting lecture, was planning on skimming it and watching small sections but I watched the whole thing without noticing the time passing!

[@bayesian7404]

He is such a great teacher on multiple topics. After this course I plan to finally take Linear Allgebra.

[@abhishekjaisinghiitr]

The best way to explain variance formula!

[@tawlguy123]

I really love the teachers at MIT. I have watched a ton of lectures from them and all have been great

[@NazriB]

Lies again? Support Indonesia Malaysia

[@rasterbate87]

Makes even high level material understandable to a neophyte. That's the mark of a skilled educator.

[@dark_all_day9311]

Extremely Based series of lectures. Top tier professor!

[@Hari-888]

What a great teacher. Absolutely loved it

[@xichenjiang7799]

Hint: Playing on 1.25 speed is ideal for this video.

[@AbdulRabChachar]

Thanks. :))

[@samvandhapathak2167]

2x for engineering students in south asia

[@Matze27396]

For an foreign student from germany like me - 1.0 speed is good. But for all native english speakers i think he speaks quite slow.

[@mlsivaprasad]

But 1.0 speed is too good.

[@pipertripp]

pro-tip, mate. Thx for the time back.

[@user-iq8ne8jh4v]

The explanation is clear, his lecture is great!

[@owenmurphy2275]

Should of done better in highschool and went to MIT. This is great. A true teacher

[@TheMaverickanupam]

Beautifully done.

[@ktiwari31]

What a treat to watch him teach! :) Hats off!!

[@kccchiu]

I had so much more fun learning the subject with Dr. Guttag than my uni professor.

[@osvaldodanielmutuque3349]

I am so grateful of your explanation

[@pajeetsingh]

Thank you Professor Guttag and thank you late Stanislaw Ulam.

[@riasejakpor6081]

Professor, your lecture was engaging. Thank you.

[@JohnSmith-he5xg]

Thanks for addressing the apparent contradiction of the Gambler's Fallacy vs Regression to the Mean ~25:00 in. I'd always thought these 2 were in opposition, but guess I'd never heard (or thought of it) in the right frame of reference.

[@hyungsubkim6525]

Thanks for this video. Amazing explanation!

[@OlumideOni]

This is the best lecture I have ever seen on statistics. It wasn't even what I was looking for but couldn't take my eyes off it till the end. Thank you Professor! Thank you MIT!

[@martinsahmed9107]

i love you sir. you are a great teacher.

[@CKPSchoolOfPhysics]

Fortunate to find his video !! A legend I was looking for !!❤️❤️❤️

[@IonidisIX]

Suddenly the Stats I did on a Data Science Coursera course start to make sense. A couple of more lectures by him and I will have everything sorted out in my mind... My God. Some lecturers just Got it and some just Don't.

[@JonathanKandell]

Love your Data Table hack at 2'. Thank you for that!

[@NickBond007]

Thank you professor Guttag. Fantastic lecture and explanations.

[@menelikm9779]

Thank you Eric.

[@alexandremelo8299]

He is the best! Such a pleasure and luck to be able to access this lecture.

[@pravink1156]

A good session, I'll search for the prof and watch more videos. 👍

[@wentaoqiu4072]

Ok, he is really good 33:45, how I hoped to have a prof. like him back in college.

[@olayinkaathenaeniolorunda8579]

Thanks for this video, very easy to understand

[@MJ-iy4fb]

I give this professor two thumbs up. I like his style. Good presentation also. A hardy bravo zulo to the man.

[@studywithjosh5109]

I was excited for this one

[@louco2]

Thank you! Amazing teacher.

[@danishsheikh8468]

Amazing explanation

[@user-ht7gw9ww1c]

My big interest is Monte Carlo simulation and Markov chain!!!

[@TheEngineeringToolboxChannel]

Excellent lecture

[@camilaisaton3988]

Adorei a aula, excelente!

[@mikepiazza2000]

I feel like I with no prior knowledge just intuitively already understand all of this and use it in daily life. Cool to hear it's basis though and a more technical presentation

[@PankajKumar-ji1ig]

take care professor and thank you for lecture

[@AugustMichael1985]

This professor is incredible!

[@NoName-jj1lv]

I like this professor a lot

[@r0b891]

Awesome lecture; thanks!

[@migzleon4047]

Thank you for sharing the knowledge...

[@user-cl1pd9im1f]

Thats the best lecture I have ever seen.

[@MichaelGotiashvili]

Great lecturer! Amazing!

[@Simbabaa]

Thank you , professors.

[@shrinwanturaha9973]

that is amazing

[@isaacspark]

Wow..... He truly explained what monte carlo simulation in 50 min. Thank you Prof.

[@guestimator121]

+Isaac Park I've heard everything but a Monte Carlo here. Confidence intervals, regression to the mean, Gambler's Fallacy etc, but not much about Monte Karlo and its many alghorithms.

[@jeroenritmeester73]

Fantastic lecture

[@dpauni]

what a wonderful teacher!! he teaches how to teach

[@syncopowerstations]

I love a professional, whether he be a doctor or a scientist, who has the confidence and grace to admit that he makes an honest mistake.

[@haneulkim4902]

Thanks!!! amazing lecture!

[@danwiberg3778]

Didn't understand any of it but I appreciate the teacher's methods. Well done.

[@jojo3451]

Genius teacher! Just so intuitive!! Wowwwww

[@reedcurtis1588]

This guy is an awesome guy

[@zini85]

Excellent class

[@69Neoares69]

I think if you add captions for the questions it will be awesome.

[@dobariyahardik3388]

thanks, sir your lecture helps me allot