Monte Carlo simulation methods apply a random sampling and modifications can be made of this method is by using variance reduction techniques (VRT). VRT objective is to reduce the variance due to Monte Carlo methods become more accurate with a variance approaching zero and the number of samples approaches infinity, which is not practical in the real situation (Chen, 2004). These techniques are ...

Ordinal data is omnipresent in almost all multiuser-generated feedback questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent o...

Carefully designed Java applications turn out to be efficient and platform independent tools that can compete well with classical implementations of statistical software. The project presented here is an example underlining this statement for random variate generation. An end-user application called RAGE (Random Variate Generator) is developed to generate random variates from probability distri...

We consider anchored Gaussian ℓ-simplices in the d-dimensional Euclidean space, that is, simplices with one fixed vertex y∈Rd and remaining vertices X1,…,Xℓ randomly sampled from d-variate standard normal distribution. determine distribution of measure such for any d, ℓ, anchor point y, which is interest, e.g., when studying asymptotic behaviour U-statistics based on simplex measures. provide t...

Agriculture is the foundation of many countries' economies, particularly in India and Tamil Nadu. The young generation who are new to farming may confront challenge not understanding what sow reap benefit from. This a problem that has be addressed, it one we addressing. Predicting proper crop production will aid making better decisions, reducing losses managing risk price fluctuations. existing...

The pathway model of Mathai (2005) mainly deals with the rectangular matrix-variate case. In this paper the scalar version is shown to be associated with a large number of probability models used in physics. Different families of densities are listed here, which are all connected through the pathway parameter α, generating a distributional pathway. The idea is to switch from one functional form...

A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary. Simulations with sixteen real-world pattern classi®cation tasks have shown that in small-scale patte...

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein’s...

We present the Optimizing Control Variate (OCV) estimator, a new estimator for Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows multiple importance sampling functions to be combined in one algorithm. Its optimizing nature addresses a major problem with control variate estimators for rendering: users supply a generic correlated function which is optimized for each...