1k citations · 1.3k across the 26 of their papers we have counts for
10 papers · 1 filter
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…
Bayesian Calibration for Activity Based Models
Laura Schultz, Joshua Auld, Vadim Sokolov
We consider the problem of calibration and uncertainty analysis for activity-based transportation simulators. Activity-Based Models (ABMs) rely on statistical modeling of individua…
Housing Market Forecasting using Home Showing Events
Yuanyuan Zha, Susan T. Parker, James J. Foster +1
Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. Both buyers and sellers make decisions without knowing th…
Eco-Mobility-on-Demand Fleet Control with Ride-Sharing
Xianan Huang, Boqi Li, Huei Peng +2
Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explo…
Strategic Bayesian Asset Allocation
Vadim Sokolov, Michael Polson
Strategic asset allocation requires an investor to select stocks from a given basket of assets. The perspective of our investor is to maximize risk-adjusted alpha returns relative…
Effects of Predictive Real-Time Traffic Signal Information
Vadim Sokolov, David W. Etherington, Christian Schmid +3
This paper analyzes the impact of providing car drivers with predictive information on traffic signal timing in real-time, including time-to-green and green-wave speed recommendati…
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…
Deep Learning for Short-Term Traffic Flow Prediction
Nicholas Polson, Vadim Sokolov
We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using regula…
Bayesian Particle Tracking of Traffic Flows
Nicholas Polson, Vadim Sokolov
We develop a Bayesian particle filter for tracking traffic flows that is capable of capturing non-linearities and discontinuities present in flow dynamics. Our model includes a hid…
Bayesian analysis of traffic flow on interstate I-55: The LWR model
Nicholas Polson, Vadim Sokolov
Transportation departments take actions to manage traffic flow and reduce travel times based on estimated current and projected traffic conditions. Travel time estimates and foreca…