![simple weighted standard deviation simple weighted standard deviation](https://slidetodoc.com/presentation_image/824876f59987f10db70ee58cb3926b28/image-8.jpg)
Suppose that you have heteroskedasticity of known form, where the conditional error variances are given by j 2 t. Note in particular that the top of the equation output shows the use of HAC covariance estimates along with relevant information about the settings used to compute the long-run covariance matrix. HAC standard errors & covariance (Bartlett kernel, User bandwidth = 8.0000) Long-run Covariance Estimationĭependent Variable: 100*D(LOG(POJ)) Method: Least Squaresĭate: 04/14/09 Time: 14:27 Sample: 1950:01 2000:12 Included observations: 612 Using Information Criteria as a Guide to Model Selection.Specifying a State Space Model in EViews.Estimating Quantile Regression in EViews.Forecasting with Nonlinear and PDL Specifications.Forecasting from Equations with Expressions.Forecasts with Lagged Dependent Variables.Limited Information Maximum Likelihood and K-Class Estimation.Whilst the principle is valid, that actual function should be thoroughly tested in practice. This attempts to estimate the number of opponents you are likely to face when you act pre-flop, and is most significant when you are in early position. Nopponentscalling+nopponentsraising+nopponentschecking*floppct+0.49 //make an estimate for opponents who have still to act (nopponentscalling+nopponentsraising+nopponentschecking*floppct+0.49)<1 ? 1 : //this should never happen, but. (handsplayed<10)? nopponentsplaying : //fallback until floppct becomes reliable (br>1)? nopponentsplaying : //just use actual opponents post-flop (nopponentsplaying=0) ? 0 : //zero if no opponents A suggestion for an f$P function, if you use prwin pre-flop, could be: prwin weighting allows for this, and therefore a prwin bot is best coded using the actual number of opponents playing. The “P factor” established in your f$P function is in fact a kludge to compensate for the fact that opponents who fold have weaker hands. You may need to change your f$P function if using weighted prwin. If your opponents at flop include a big blind who has not been raised, no weighting will be applied to his hand, since no assumptions can be made about it. It will also be turned off if your f$P function specifies more than 13 opponents. If f$willplay is absent, or returns a zero value, the prwin weighting is turned off. For DLL developers, there is also the option to use ) which is based on published data about hands players actually take to flop, and varies from conventional ones giving win probabilities, particularly in the way it favors suited hands! In the absence of statistical support it is not recommended that you implement f$topclip or f$mustplay unless you have a good picture of the standard of play at a table. OpenHoldem’s “Weighted prwin” is based on a fairly simple model using pre-flop behavior. Essentially it aims to put your opponents on cards as accurately as possible.
![simple weighted standard deviation simple weighted standard deviation](http://i.ytimg.com/vi/FefJ1paq750/hqdefault.jpg)
Factors which can be used to make inferences about cards include willingness to pay to see flop, betting behavior when faced by common cards, and historical information about opponent behavior.
![simple weighted standard deviation simple weighted standard deviation](https://www.statology.org/wp-content/uploads/2021/02/weighted_sd_excel2.png)
The prwin values you obtain will be within 1 standard deviation 67% of the time, within 2 standard deviations 95% of the time, and within 3 standard deviations 99% of the time.Ī weighted prwin is one which is calculated by making informed selections of the cards that opponents are likely to hold, and using these cards in the calculation of your win probability. In our case, it would be 100,000 required iterations. = 0.001).įind that standard deviation in the top chart and read off the number of iterations required. To get 1 standard deviation, divide by 3 (to give std. For example, if you are happy with getting prwin's of 0.846 - 0.852 99% of the time for AA headsup preflop, your error would be (0.849) +- 0.003.
#SIMPLE WEIGHTED STANDARD DEVIATION HOW TO#
Here is how to use the data in the above graphs:ĭecide what error you find acceptable 99% of the time. Standard deviation of prwin/prtie/prlos by Number of Iterations (NIT)įigure 5.21‑59 Standard Deviation by Iterations
![simple weighted standard deviation simple weighted standard deviation](https://ars.els-cdn.com/content/image/3-s2.0-B9780128028179000167-f16-05-9780128028179.jpg)
This material was originally produced by BuckyBall and can be found here: