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.
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.
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 🐇🐰🐰
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 😃🐇🐰
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
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
