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

[@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.

[@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

[@mdcamp00]

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

[@hamidrajabi8775]

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

[@pepegallardo4060]

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

[@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

[@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****.

[@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.

[@ractheworld]

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

[@habeebyusuf7037]

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

[@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

[@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??

[@satria5403]

Thank you for share this amazing video

[@yusuffarah351]

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

[@paulmctaggart6947]

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

[@CodeJeffo]

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

[@annakh9543]

he is so funny, i wish i had such professors

[@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.

[@satoshinakamoto171]

such respect for these fantastic teachers

[@jerryreed2050]

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

[@fabbiotec]

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

[@OmarMagdyNofal]

Actually you are an amazing demonstrator

[@GbUnLimiteD]

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

[@longn.8804]

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

[@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.

[@georgejetson9801]

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

[@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.

[@androsram641]

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

[@melgibsonsmyhero]

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

[@abhishekjaisinghiitr]

The best way to explain variance formula!

[@JebBradwell]

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

[@dennisangelomarasigan2431]

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

[@anthonycicero6102]

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

[@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”

[@markimark8445]

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

[@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.

[@dark_all_day9311]

Extremely Based series of lectures. Top tier professor!

[@bayesian7404]

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

[@hyungsubkim6525]

Thanks for this video. Amazing explanation!

[@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.

[@rasterbate87]

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

[@NickBond007]

Thank you professor Guttag. Fantastic lecture and explanations.

[@Hari-888]

What a great teacher. Absolutely loved it

[@martinsahmed9107]

i love you sir. you are a great teacher.

[@gustavogodoy5823]

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

[@nikolavalizadeh133]

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

[@alperensayar9679]

Hayatımdaki en iyi üniversite dersiydi.Thanks Prof J. Guttag

[@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

[@TheMaverickanupam]

Beautifully done.

[@user-iq8ne8jh4v]

The explanation is clear, his lecture is great!

[@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

[@louco2]

Thank you! Amazing teacher.

[@kccchiu]

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

[@ktiwari31]

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

[@riasejakpor6081]

Professor, your lecture was engaging. Thank you.

[@paulorufalco]

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

[@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.

[@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.

[@osvaldodanielmutuque3349]

I am so grateful of your explanation

[@MJ-iy4fb]

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

[@JonathanKandell]

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

[@AugustMichael1985]

This professor is incredible!

[@alexandremelo8299]

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

[@pajeetsingh]

Thank you Professor Guttag and thank you late Stanislaw Ulam.

[@CKPSchoolOfPhysics]

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

[@shrinwanturaha9973]

that is amazing

[@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

[@studywithjosh5109]

I was excited for this one

[@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!

[@menelikm9779]

Thank you Eric.

[@wentaoqiu4072]

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

[@user-cl1pd9im1f]

Thats the best lecture I have ever seen.

[@camilaisaton3988]

Adorei a aula, excelente!

[@user-ht7gw9ww1c]

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

[@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.

[@olayinkaathenaeniolorunda8579]

Thanks for this video, very easy to understand

[@owenmurphy2275]

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

[@pravink1156]

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

[@danishsheikh8468]

Amazing explanation

[@QuantAgent]

The true meaning of the teacher!

[@PankajKumar-ji1ig]

take care professor and thank you for lecture

[@Simbabaa]

Thank you , professors.

[@migzleon4047]

Thank you for sharing the knowledge...

[@GPCTM]

proper: denoting a subset or subgroup that does not constitute the entire set or group, especially one that has more than one element.

[@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.

[@jojo3451]

Genius teacher! Just so intuitive!! Wowwwww

[@TTVbygowtham]

Concept well explained

[@creedrituel]

This is what is used to determine results of A/B testing folks, i had to learn this on the fly at my job

[@franciscoc1144]

i love this guy

[@NoName-jj1lv]

I like this professor a lot

[@davidwilkie9551]

Very good introduction of how the e-Pi-i conception of probabilistic Calculus by Pi circularity numberness/orbital is a dualistic +/- possible Infinite Sum, Normal/orthogonal self-defining "e", metastable +/- singularity convergence to zero difference, balance of frequency constants in Totality.

[@MichaelGotiashvili]

Great lecturer! Amazing!

[@NourAllahNasr]

Not what I was looking for, however very interesting and useful video, I will see more, thanks

[@dpauni]

what a wonderful teacher!! he teaches how to teach

[@smartestmansays2157]

I am the Great Canadian Gambler and can attest that my biggest two 6.2 Standard Deviation swings ever were back to back. Same in my early years when I played Craps to get the free junket to the casinos. Biggest win followed by biggest loss. I note that because I heard poker champ Daniel Negreanu mention the same back-to-back phenomenon. Always believed in the odds but back-to-back streaks leave an eerie feeling.

[@reedcurtis1588]

This guy is an awesome guy

[@haneulkim4902]

Thanks!!! amazing lecture!