What is Monte Carlo Simulation?
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- Опубліковано 31 тра 2024
- Learn more about watsonx: ibm.biz/BdvxDh
Monte Carlo Simulation, also known as the Monte Carlo Method or a multiple probability simulation, is a mathematical technique, which is used to estimate the possible outcomes of an uncertain event. Watch master inventor Martin Keen explain how Monte Carlo Simulation may be your closest thing to a time machine - or more simply - how it may be a way to look into the future for making business decisions.
#AI #Software #ITModernization #MonteCarloSimulation #lightboard #IBM
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…
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!
So there's much more to this man than beer, interesting.
Impeccable explanation, thank you.
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.
Thanks for sharing this content!
Thank you so much ❤️ I need this!
awesome explanation!!!
Could Monte carlo simulation be used to predict fatigue life of parts based upon historical data?
Thank you ❤
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 😃🐇🐰
You should have used the dice example to example the three items of interest at the end
very interesting! what is the software to use for the simulation?
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 🐇🐰🐰
@@amaarmarco530 how would you generate the initial variables and weights in the simulation itself, is that even possible?
Wont you just get back to the input prob distributions?
Very nice
Wonderfully Explained. Looking for more great content from your side.
This guy is a pro at writing backwards legibly.
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
Thanks super awesome, can we apply it to our own life!
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 had me at wordle
Neumont College of Computer Science checkin in
Please create a video tutorial about coagulation and how to simulate
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 UA-cam brewer!
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.
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
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?
So that’s how they predict the weather !
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".
Sounds like kalman filter with random inputs
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With numeric methods and a 69 inputs of last lotto sequence this piece of technique can predict at least 3 numbers from lotto
So Monte Carlo simulation is what ancestors knew as Astrology = Data Science
Como el copi la explicación
Wow, I'm trying to code my neural network right now but monte carlo seems like the next path of evolution
Random sampling
I am a beginner, and cannot understand a thing
全天候海洋监听系统 (SOSUS)
No.
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
Lame.