Pratik NagUniversity of Wollongong | UOW · School of Mathematics and Applied Statistics (SMAS)
Pratik Nag
Doctor of Philosophy
About
12
Publications
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Introduction
I am a Research Fellow at National Institute of Applied Statistical Research (NIASRA), University of Wollongong, working with Prof. Noel Cressie, Prof. Andrew Zammit Mangion, and Prof. Sumeetpal Singh. My research is mainly focused on the Bayesian implementation of next-generation neural networks, including Fourier Neural Operators. I completed my PhD at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Ying Sun.
Publications
Publications (12)
![Figure 4. Same-Different exercise example [12]](profile/Pratik-Nag-2/publication/387670382/figure/fig3/AS:11431281301107644@1735874020925/Same-Different-exercise-example-12_Q320.jpg)
Music generation has been established as a prominent topic in artificial intelligence and machine learning over recent years. In most recent works on RNN-based neural network methods have been applied for sequence generation. In contrast, generative adversarial networks (GANs) and their counterparts have been explored by very few researchersfor mus...
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertaint...
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertaint...
High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogen...
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction. These techniques typically presuppose that the data are observed from a stationary GP with parametric covariance structure. However, processes in real-world applications often exhibit non-Gaussianity and nonstationarity. Moreover, likelihoo...
Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques...
In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on...
In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on...
