Monte Carlo Simulations : Data Science Basics

Solving complex problems using simulations

0:00 Easy Example
4:50 Harder Example
13:32 Pros and Cons of MC

[aaryadeshpande]

Unbelievable, every second of this 19-minute, 13-second video was characterized by a very clear sequence of sentences that meshed together, without any mistake, to form a very crafted, well-understood explanation of all the ideas together. For instance, what makes the difference between most who explain and those like you is found here at 15:38: "Because when I'm in this loop, this is going to run for a long time when p is large, because it's barely ever gonna see two loses in a row." I was following along but it didn't completely click in a concrete sense until you grounded the logic with saying "because it's barely ever gonna see two loses in a row." You founded all explanations with some set of "extra words" that made everything you say click with minimal rewinding and mental pounding to understand them, and I find that two types of exist in this world, those who consistently don't employ those extra words and those who consistently do incorporate them. I might nickname that feature the "connecting back to concrete-land" or "those extra few click words" whose presence or absence in speech make the difference between it clicking to an audience and it not. And the absence of such extra words, when multiplied over the many chunks of speech to hundreds of someone's utterances over a video or lecture, can culminate to a superficial understanding where you are left in abstract land but have an itchy, uneven feeling of why the circle around the explanation didn't make a complete closure of concreteness or a filling understanding. Those extra words, the attention to detail, and ensuring your learners really following along all the way to the end and closure of the circle of the explanation you intend to communicate really makes the difference in reaching a wider audience and making critical concepts well-understood. Thank you for the video!!

[stephenpuryear]

Superb! What is so remarkable in your presentation is the balance that you bring to the challenge. Each approach has strengths and weaknesses and it is essential to keep those in mind at the start. We are currently weathering a huge storm in which someone advocates for "their" method and says explicitly (or strongly hints) that every other approach is garbage or "not science". That makes your channel a big breath of fresh air! Thank you!

[stephenpuryear]

Another cool thing about your approach is that your "toy example" follows closely from Baye's original paper in which discussed tossing balls onto a flat surface and then locating an unknown point more and more closely. Thank again for these great videos!

[NinjaAdorable]

I use MC for some fabrication process variation simulations in my research, and I must say this is one of the better explanations for the simulation. Good job.

Couple of things:
1) The rules, emerge from understanding the problem. In my case the behavior of the material, possible probability distribution of faults (from case studies of real fabrication imperfections). You use MC when you know what the real world solution would look like but you need a more approachable, repeatable, or approximate approach to the solution (Eg: I cannot just fab the chip every time I want to see the impact of fabrication imperfections).

2) not everything has an analytical solution, or more correctly saying, the solution is very complex, having to consider various factors to reach the "true" solution. It's in scenarios like this where the application of MC truly shines.

[rezvanbahmani]

This channel is the BESTESTESTESTEST data science channel I've ever watched. AMAZINGLY wonderful. THANKS dude

[alihaghverdii]

Thank you for explaining the method as simply as possible. AWESOME!

[Reach]

I've wondered about the Monte Carlo method for a long time, but never needed to figure out what it was. Now I know what it is, and see some uses in my robotics hobby. Thanks!

[murtazakhasamwala]

00:03 Monte Carlo method is a practical concept in data science, illustrated with examples and discussing advantages and disadvantages.
01:41 Monte Carlo simulation using 1 million points to calculate the probability of darts landing in a circle.
03:26 Using Monte Carlo Method to estimate the number of points inside a circle
05:10 Expected number of rounds to play until losing two times in a row
06:52 Analyzing the expected number of rounds in a game with three simple cases.
08:44 Expected number of rounds calculation using algebra
10:34 Simulation of game rounds using Monte Carlo method.
12:23 Monte Carlo simulation provides a relatively easy way to get close to the answer for difficult problems.
14:02 Monte Carlo methods allow non-experts to solve problems and can be easier than analytical methods.
15:48 Monte Carlo methods can take a really long time and become impractical to use.
17:21 Monte Carlo methods are not interpretable.
18:52 Choosing between simulation and analytic approach in Monte Carlo Methods

[minhaoling]

Your videos are so clear and simple and I believe you will be the top youtube in data science concepts in the near future. One good suggestion will be to provide a snapshot of the things you wrote on board.

[pectenmaximus]

Wonderful presentation. Ticks all the boxes. Look forward to watching your other videos.

[imomjonkhamid]

Thank you bro, your contribution benefits many people from developing countries, God bless you

[johnlourdusamy]

I am not an expert, but I enjoyed the dart and circle/square explanation. You should start something like khan academy.

[anthnyalxndr]

Thanks for taking the time to put this together 🤝

[spicytuna]

again, thanks for the splendid easy to understand explanation.
coming up with a recursive formulae seems very challenging.

[ANJA-mjto]

THANKS !
Despite the many desierprperties of the aforementioned methodolist, the Monte Carlo method is still the most general and reliable sgohasgic method

[nicololucchesi]

Very cool analytical solution to the dazzling problem you proposed, congrats on the video!

[Motherdaughterdancetogether]

love all the videos I've watched so far! very easy for beginners to follow and learn!

[alibaba]

Wonderful video, just what I needed.

- Has exampleS (1<)
- Simple
- Question and answers
- Objective teaching

[akshikaakalanka]

As always, amazing explanations. Thank you!

[NickKravitz]

Great video as always! Backgammon and poker best play situations cannot be solved via closed form and therefore are mostly solved by MC (named rollout and solver respectively) Now that you taught us MC you have to teach MCMC (Markov Chain Monte Carlo) next!