Video is inverted: 1. For one, most people wear their watch on the left hand. 2. Assuming that is an apple watch, the knob is in the opposite side indicating an inverted video.
Dammmmmmmm you saved me.......... thank you so much!!! I was searching the whole internet to prepare for my school project about this... thanks..... This helped me the most
Great video. I'm wondering if this can be applied to time series forecasting based on historical data. For example forecast the demand of a product based on the price, number of units sold in a specific month etc. ¿Does anyone knows?
YES , It Can Do that , But it needs some initial variables to work with ,Some variables that are important to that are ,variables related to Game theory (other players in market) , Need or Demand metric (simply how needed or demanded the product) , local(individual) economical chain , And maybe another one or two variables, a Combination of Higher Values of 1st Variable And 2nd variable Will produce More elasticity in Demand (negative elasticity ), a Combination of Higher Values of 2nd variable , lower 2nd variables and Higher 3rd variable , The Less elasticity will be (Stable equilibrium), a combination between High 1st variable , High 2nd And 3rd variable , Will produce a Result of a Mid elasticity level of demand (Semi Stable equilibrium ), You Can introduce new variables to the mix , And The smarter you are observing levering-variables (High-weighted-variables) the More Accurate your model will be 🐇🐰🐰
How do you title a video "what is..." without ever demonstrating What Is the thing you're talking about? I didn't click to find out who uses Monte Carlo simulations. I clicked to understand how I might use one. There was zero instructive information in this video.
Thats a nice & crisp way of explaining to the point, thanks. But i was searching for a simulation, where it should optimize & estimate the better Sequence of different model mix Feed in a manufacturing line (single piece flow) but each model has different Process completion Cycle times from Feed to Final stage. It would be great help, if you can suggest a better way of handling this problem ?!
1-Separate every manufacturing Cycle for Every product-component 2-Build a Time series for Every INDIVIDUAL Component -(be careful of dependant variables time sequence) 3-Build a Time series for CURRENT Combination of ALL components till end product is produced (Be CAREFUL with dependant variables time sequence ) 4-Upload all data And time sequences into a Computer , Buy a Manufacturing Simulation program , upload all data, Run Simulation on ALL possible combination to final production 5-Upload all result to a Statistical-Calculator 6-Choose the one with the LEAST Time sequence of production 😃🐇🐰
Markov Chain Monte Carlo (MCMC) are a set of Monte Carlo techniques for effective sampling that are useful when the distribution is maybe high dimensional, unormalized (so you know that it is proportional to some quantity, but you don't know what exactly) or maybe just difficult to sample from for other reasons. The way these generally work are that when you have a sample with high probability, you will try to sample something close to that. In the example of tossing two dice (not a great example in this case because dice are discrete), the value with highest probability is 7, so when you simulate a seven, you are likely to get other values with high probability that are close to it next, like 6. The values that you're least likely to sample are the low probability ones, like 1 and 12. A few examples of MCMC algorithms are Metropolis-Hastings (probably the most general one), Gibbs, Langevin Monte Carlo and Hamiltonian Monte Carlo. The video does a very poor job in explaining what Monte Carlo is btw
He’s really good at writing backwards
Search on "lightboard videos".
I expect he writes the right way round and they just mirror the video, but I would love your take to be true!
you are really good at stupid judgements
So there's much more to this man than beer, interesting.
Great content! Also this is the first time I have seen someone writing in reversal in front of a screen, which is amazing!
or they just inverted the video lol
Lol yeah they use a mirror
I get what you mean. I came to the comments to type the same thing.
Video is inverted:
1. For one, most people wear their watch on the left hand.
2. Assuming that is an apple watch, the knob is in the opposite side indicating an inverted video.
This guy just casually writing backwards on a piece of glass
no lol
You are seeing mirror flip of actual footage
Made it look easy
And he's left handed! He's just like Leonardo da Vinci 😂
Or the video is mirror flipped
I was thinking about this the whole time…
Dammmmmmmm you saved me.......... thank you so much!!! I was searching the whole internet to prepare for my school project about this... thanks..... This helped me the most
Impeccable explanation, thank you.
🎯💎🏆 Great insightful & fruitful video 🏆💎🎯
लोकः समस्ताः सुखिनो भवन्तु
( May all beings lead prosperous life across Globe 🌍 )
If I remember correctly, this simulation was used in the AlphaGo program back in 2016.
This guy is a pro at writing backwards legibly.
Great video. I'm wondering if this can be applied to time series forecasting based on historical data. For example forecast the demand of a product based on the price, number of units sold in a specific month etc. ¿Does anyone knows?
Isn't that how we predict inflation and deflation. GDP is based on this aspect.
Thank you for posing this question, developed my understanding of Monte Carlo better.
YES , It Can Do that , But it needs some initial variables to work with ,Some variables that are important to that are ,variables related to Game theory (other players in market) , Need or Demand metric (simply how needed or demanded the product) , local(individual) economical chain , And maybe another one or two variables,
a Combination of Higher Values of 1st Variable And 2nd variable Will produce More elasticity in Demand (negative elasticity ),
a Combination of Higher Values of 2nd variable , lower 2nd variables and Higher 3rd variable , The Less elasticity will be (Stable equilibrium),
a combination between High 1st variable , High 2nd And 3rd variable , Will produce a Result of a Mid elasticity level of demand (Semi Stable equilibrium ),
You Can introduce new variables to the mix , And The smarter you are observing levering-variables (High-weighted-variables) the More Accurate your model will be 🐇🐰🐰
@@Discordxeroxero6454 how would you generate the initial variables and weights in the simulation itself, is that even possible?
Lol love the interactiveness you had with the video. I just want to know how the hell did you write backwards?! That's crazy impressive haha
This is exactly what I was wondering about!
You should have used the dice example to example the three items of interest at the end
Thanks for sharing this content!
Hey isnt this the guy who brews beer?? This is Martin from The Homebrew Challenge!
Imagine my surprise when I'm trying to research this very technical subject and come upon my favorite RUclips brewer!
Thank you so much ❤️ I need this!
awesome explanation!!!
We use it in nuclear physics in particle collisions. You can use it in math to estimate integrals, and so on.
Hello, I'm just curious how exactly does monte carlo work in particle collisions?
How do you title a video "what is..." without ever demonstrating What Is the thing you're talking about? I didn't click to find out who uses Monte Carlo simulations. I clicked to understand how I might use one. There was zero instructive information in this video.
very interesting! what is the software to use for the simulation?
Thank you ❤
So that’s how they predict the weather !
Neumont College of Computer Science checkin in
Can you please provide use case of a MC simulation for risk management of a Construction Capital Project?
Could Monte carlo simulation be used to predict fatigue life of parts based upon historical data?
Wonderfully Explained. Looking for more great content from your side.
Thats a nice & crisp way of explaining to the point, thanks. But i was searching for a simulation, where it should optimize & estimate the better Sequence of different model mix Feed in a manufacturing line (single piece flow) but each model has different Process completion Cycle times from Feed to Final stage. It would be great help, if you can suggest a better way of handling this problem ?!
1-Separate every manufacturing Cycle for Every product-component
2-Build a Time series for Every INDIVIDUAL Component -(be careful of dependant variables time sequence)
3-Build a Time series for CURRENT Combination of ALL components till end product is produced (Be CAREFUL with dependant variables time sequence )
4-Upload all data And time sequences into a Computer , Buy a Manufacturing Simulation program , upload all data, Run Simulation on ALL possible combination to final production
5-Upload all result to a Statistical-Calculator
6-Choose the one with the LEAST Time sequence of production 😃🐇🐰
Thanks super awesome, can we apply it to our own life!
Very simple and straightforward explanation but would have been better had you provided a real life example
Very nice
You had me at wordle
Wont you just get back to the input prob distributions?
With numeric methods and a 69 inputs of last lotto sequence this piece of technique can predict at least 3 numbers from lotto
good video technique
oh that friend with a gambling addiction loves this
Please create a video tutorial about coagulation and how to simulate
is this the same principle underlying how neural nets are able to predict things?
No. But monte carlo can be used in conjunction with neural networks in some specific settings
Is Monte Carlo Simulation same as Markov Chain Monte Carlo Simulation?
Markov Chain Monte Carlo (MCMC) are a set of Monte Carlo techniques for effective sampling that are useful when the distribution is maybe high dimensional, unormalized (so you know that it is proportional to some quantity, but you don't know what exactly) or maybe just difficult to sample from for other reasons.
The way these generally work are that when you have a sample with high probability, you will try to sample something close to that. In the example of tossing two dice (not a great example in this case because dice are discrete), the value with highest probability is 7, so when you simulate a seven, you are likely to get other values with high probability that are close to it next, like 6. The values that you're least likely to sample are the low probability ones, like 1 and 12.
A few examples of MCMC algorithms are Metropolis-Hastings (probably the most general one), Gibbs, Langevin Monte Carlo and Hamiltonian Monte Carlo.
The video does a very poor job in explaining what Monte Carlo is btw
how is he writing the letters. its bothering me trying to figure out
Search on "lightboard videos".
아저씨 사랑해요..........ㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜㅜ
Sounds like kalman filter with random inputs
So Monte Carlo simulation is what ancestors knew as Astrology = Data Science
Wow, I'm trying to code my neural network right now but monte carlo seems like the next path of evolution
Como el copi la explicación
I am a beginner, and cannot understand a thing
Random sampling
No.
全天候海洋监听系统 (SOSUS)
Can we predict the future?
Of course NOT, otherwise models become helpless and many people getting richer quicker. So, it's boring.
What does 6*6*1000 give you? That number doesn't do anything. This video teaches nothing.
Kinda does buddy, I’m doing the CFA level 1 and it did help. Cheer up 🎉
weird and somewhat misguiding
random bs go
Lame.