Timeline for Monte Carlo simulation vs. machine learning algorithms: what is the difference in application?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 19, 2014 at 23:44 | vote | accept | sunboya | ||
| Feb 19, 2014 at 12:45 | comment | added | Quartz | You could even generate random outcomes ("MC") from a model estimated by some ML technique (maybe to check afterwards how well the model matches the original data, e.g. comparing realized to synthetic empirical distributione). | |
| Feb 19, 2014 at 12:44 | comment | added | Quartz | Please read again, the comparison you're asking for is not meaningful. The samples generated via MC are synthetic, artificial, only as good as the model they come from and therefore need not to adhere to the real data, let alone improve over it. It cannot predict anything more than the chosen model. You cannot compare MC to ML techniques: these generate descriptive/predictive models, MC requires a model from the start and does not give you one. | |
| Feb 18, 2014 at 21:36 | comment | added | sunboya | Thanks for both your answer and comment, just digging a little deeper: "a numerical tool to obtain samples from a given model" to increase the amount of data you have for modelling?? If I was going to predict the outcome of any given event, for sake of arguement lets say 'motor car racing' we have all of our required parameters and data representing the last 20 years of motor car races. How would MC be used to enable you to best predict who would win the next motor car race? And would it be more or less accurate than the ML techniques previously mentioned? | |
| Feb 18, 2014 at 9:43 | comment | added | Quartz | You could give us more context on what made you ask such a question, it probably arises from one such application of MC in inference. | |
| Feb 18, 2014 at 9:43 | history | answered | Quartz | CC BY-SA 3.0 |