67 citations · 71 across the 7 of their papers we have counts for
Showing stat.APShow all
3 papers · 1 filter
stat.AP2022★ 1 cited
Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs
Laura Schultz, Vadim Sokolov
Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Compu…
stat.AP2022
On the Probability of Magnus Carlsen reaching 2900
Sohan Bendre, Shiva Maharaj, Nick Polson +1
How likely is it that Magnus Carlsen will achieve an Elo rating of ? This has been a goal of Magnus and is of great current interest to the chess community. Our paper uses pr…
stat.AP2016
Sequential Bayesian Learning for Merton's Jump Model with Stochastic Volatility
Eric Jacquier, Nicholas Polson, Vadim Sokolov
Jump stochastic volatility models are central to financial econometrics for volatility forecasting, portfolio risk management, and derivatives pricing. Markov Chain Monte Carlo (MC…