Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Ludkovski, Mike"'
Autor:
Li, Qiqi, Ludkovski, Mike
We design a Gaussian Process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilisti
Externí odkaz:
http://arxiv.org/abs/2409.16308
We consider the construction of renewable portfolios targeting specified carbon-free (CFE) hourly performance scores. We work in a probabilistic framework that uses a collection of simulation scenarios and imposes probability constraints on achieving
Externí odkaz:
http://arxiv.org/abs/2312.07733
Autor:
Ludkovski, Mike, Padilla, Doris
We investigate state-level age-specific mortality trends based on the United States Mortality Database (USMDB) published by the Human Mortality Database. In tandem with looking at the longevity experience across the 51 states, we also consider a coll
Externí odkaz:
http://arxiv.org/abs/2312.01518
Autor:
Ludkovski, Mike, Risk, Jimmy
We develop a flexible Gaussian Process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to
Externí odkaz:
http://arxiv.org/abs/2305.01728
We develop a probabilistic framework for joint simulation of short-term electricity generation from renewable assets. In this paper we describe a method for producing hourly day-ahead scenarios of generated power at grid-scale across hundreds of asse
Externí odkaz:
http://arxiv.org/abs/2205.04736
We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed-form recursion for the optimal strategy, extending t
Externí odkaz:
http://arxiv.org/abs/2204.08581
Autor:
Ludkovski, Mike
I develop a numerical algorithm for stochastic impulse control in the spirit of Regression Monte Carlo for optimal stopping. The approach consists in generating statistical surrogates (aka functional approximators) for the continuation function. The
Externí odkaz:
http://arxiv.org/abs/2203.06539
Autor:
Huynh, Nhan, Ludkovski, Mike
We investigate jointly modeling Age-specific rates of various causes of death in a multinational setting. We apply Multi-Output Gaussian Processes (MOGP), a spatial machine learning method, to smooth and extrapolate multiple cause-of-death mortality
Externí odkaz:
http://arxiv.org/abs/2111.06631
Gaussian process (GP) surrogate modeling for large computer experiments is limited by cubic runtimes, especially with data from stochastic simulations with input-dependent noise. A popular workaround to reduce computational complexity involves local
Externí odkaz:
http://arxiv.org/abs/2109.05324
Autor:
Ludkovski, Mike
We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implement
Externí odkaz:
http://arxiv.org/abs/2012.00729