Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates. To...

It is a well known fact that at high sampling frequencies, the contamination of microstructure noise causes the Realized Variance to be a biased measure of the Integrated Variance. Recent developments in this field propose sampling on lower frequencies, sub-sampling techniques, or bias corrections using the autocorrelation patterns in the data. In this paper we propose a structural decompositio...

to determine the effects of vinasse on physicochemical properties of a calcareous and an acidic soil experiment was factorial and had a complete randomized design and three replications. factorial combinations of five vinasse levels (0, 5, 10, 20 and 40 t/ha) and three exposure times (10, 20 and 30 days) were considered as the treatments of this experiment. the result of experiment showed that ...

In this paper we investigate the effect of oil price shocks on stock market index in Iran, by using of a structural VAR (SVAR) approach. We used four variables in the model namely Kilian index, global oil supply, real oil price and real stock market index. The data are monthly and spanning the period 1997M10-2014M12. We identify the effect of four different shocks on stock market including oil ...

This paper proposes a spatially adaptive statistical model for wavelet image coefficients in order to perform image de-noising. The wavelet coefficients are modeled as zero-mean Gaussian random variables with high local correlation. This model is developed in a Bayesian framework, where a Maximum Likelihood (ML) estimator evaluates the variance of the blocks to which the wavelet subbands have b...

This paper addresses the QR decomposition and UD factorization based square-root algorithms of the recursive least-squares (RLS) Wiener fixed-point smoother and filter. In the RLS Wiener estimators, the Riccati-type difference equations for the auto-variance function of the filtering estimate are included. Hence, by the roundoff errors, in the case of the small value of the observation noise va...