例句與造句

1. Autocorrelation analysis helps to identify the correct phase of the fitted model while the successive differencing transforms the stochastic drift component into white noise.
2. In the course of the time series analysis, identification of cyclical and stochastic drift components is often attempted by alternating autocorrelation analysis and differencing of the trend.
3. This non-stochastic drift can be removed from the data by regressing y _ t on t using a functional form coinciding with that of " f ", and retaining the stationary residuals.
4. Stochastic drift can also occur in population genetics where it is known as Genetic drift . A " finite " population of randomly reproducing organisms would experience changes from generation to generation in the frequencies of the different genotypes.
5. In this case the stochastic term is stationary and hence there is no stochastic drift, though the time series itself may drift with no fixed long-run mean due to the deterministic component " f " ( " t " ) not having a fixed long-run mean.
6. It's difficult to find stochastic drift in a sentence. 用stochastic drift造句挺難的
7. So after the initial shock hits " y ", its value is incorporated forever into the mean of " y ", so we have stochastic drift . Again this drift can be removed by first differencing " y " to obtain " z " which does not drift.
8. In this case the non-stationarity can be removed from the data by first differencing, and the differenced variable z _ t = y _ t-y _ { t-1 } will have a long-run mean of " c " and hence no drift . But even in the absence of the parameter " c " ( that is, even if " c " = 0 ), this unit root process exhibits drift, and specifically stochastic drift, due to the presence of the stationary random shocks " u " " t " : a once-occurring non-zero value of " u " is incorporated into the same period's " y ", which one period later becomes the one-period-lagged value of " y " and hence affects the new period's " y " value, which itself in the next period becomes the lagged " y " and affects the next " y " value, and so forth forever.
9. But this implies ( according to the acceleration return map ) that acceleration is zero at t = 5, i . e . at t = 5 v is 30 mm / s ^ 2 with acceleration at t = 6 being anywhere from 0 to 500 mm / s ^ 2, but with a bias to accelerate it at + 450 mm / s ^ 2 towards 60 mm / s at t = 6 for the cycle to begin again . ( Stochastic drift, or chance of being an outlier, allows the fly to escape this cycle . ) But, if v = 60 mm / s at t = 2, then a = ~ + 450 mm / s ^ 2 at t = 2, which means a ~ =-450 mm / s ^ 2 and v ~ = 30 mm / s at t = 3, a ~ = 0 mm / s ^ 2 and v ~ = ~ 30 mm / s at t = 4 and v = 60 mm / s and a = 450 mm / s ^ 2 at t = 5-- another short-term loop which will be broken by drift.