Timeline for What's the point of resampling?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 28, 2022 at 3:56 | comment | added | Dave Harris | @ClaudioMoneo I generalized Ito and Stratonovich's work so that it would harmonize with Bruno de Finetti's and Wald's, with a second strand running from Kolmogorov. | |
| Apr 28, 2022 at 3:50 | comment | added | Dave Harris | @ClaudioMoneo it is. I am proposing a new calculus to replace Ito's, both a Bayesian and a Frequentist one. I used an indirect utility function and marginalized out the parameters for both types. However, only the Bayesian method complies with the Dutch Book Theorem which is essential. I basically extended Wald's decision theory and created a class of operators to replace expectations for the distributions that lack a first moment. The expectations operator is a special case of the anticipation operator. | |
| Apr 27, 2022 at 8:21 | comment | added | Claudio Moneo | @DaveHarris I was not aware of that, interesting. And yet Bayesian frameworks for asset allocation seem a lot more rigourous to me than resampling | |
| Apr 27, 2022 at 4:05 | comment | added | Dave Harris | @ClaudioMoneo also, models like the CAPM, etc violate the converse of the Dutch Book Theorem unless it is a true statement that the parameters are known. Because of that, the absence of arbitrage opportunities is excluded by theorem. | |
| Apr 27, 2022 at 4:02 | comment | added | Dave Harris | @ClaudioMoneo the CAPM, Black-Scholes, etc do not survive in a Bayesian framework for many reasons. The two biggest ones is that most Bayesian axiomatic systems do not allow countable additivity, and the integrals diverge. An assumption of the underlying calculus is that the parameters are known. Parameters are random variables in a Bayesian framework. Parameters are fixed points in the Frequentist one and models like the CAPM inherit the axioms that undergird Frequentist methods. Without them, you will not get the same answer. | |
| Apr 24, 2022 at 12:30 | comment | added | Claudio Moneo | What I would expect from a Bayesian framework is optimizing w.r.t the posterior directly. Not sampling and combining the solutions. But nevertheless thanks for your answer | |
| Apr 24, 2022 at 12:14 | comment | added | nbbo2 | That is more or less what resampling is doing, but with discrete samples rather than with a continuous probability distribution. AFAIK we can only solve the Markowitz problem for specific input values, not with prob distr as inputs. Need to ask a Bayesian expert, which I am not. | |
| Apr 24, 2022 at 8:02 | comment | added | Claudio Moneo | But wouldn't a Bayesian approach compute a posterior and then do mean-variance optimization w.r.t this posterior? | |
| Apr 24, 2022 at 7:19 | history | answered | nbbo2 | CC BY-SA 4.0 |