Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Kleppe, Tore Selland"'
Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an optimal path length requires a predefined step size, while the ideal ste
Externí odkaz:
http://arxiv.org/abs/2408.08259
Autor:
Tran, Jimmy Huy, Kleppe, Tore Selland
Three approaches for adaptively tuning diagonal scale matrices for HMC are discussed and compared. The common practice of scaling according to estimated marginal standard deviations is taken as a benchmark. Scaling according to the mean log-target gr
Externí odkaz:
http://arxiv.org/abs/2403.07495
We propose a generic approach for numerically efficient simulation from analytically intractable distributions with constrained support. Our approach relies upon Generalized Randomized Hamiltonian Monte Carlo (GRHMC) processes and combines these with
Externí odkaz:
http://arxiv.org/abs/2311.14492
Randomized Runge-Kutta-Nystr\'om Methods for Unadjusted Hamiltonian and Kinetic Langevin Monte Carlo
Autor:
Bou-Rabee, Nawaf, Kleppe, Tore Selland
We introduce $5/2$- and $7/2$-order $L^2$-accurate randomized Runge-Kutta-Nystr\"{o}m methods, tailored for approximating Hamiltonian flows within non-reversible Markov chain Monte Carlo samplers, such as unadjusted Hamiltonian Monte Carlo and unadju
Externí odkaz:
http://arxiv.org/abs/2310.07399
Autor:
Kleppe, Tore Selland
A metric tensor for Riemann manifold Monte Carlo particularly suited for non-linear Bayesian hierarchical models is proposed. The metric tensor is built from symmetric positive semidefinite log-density gradient covariance (LGC) matrices, which are al
Externí odkaz:
http://arxiv.org/abs/2211.01746
agtboost is an R package implementing fast gradient tree boosting computations in a manner similar to other established frameworks such as xgboost and LightGBM, but with significant decreases in computation time and required mathematical and technica
Externí odkaz:
http://arxiv.org/abs/2008.12625
An information theoretic approach to learning the complexity of classification and regression trees and the number of trees in gradient tree boosting is proposed. The optimism (test loss minus training loss) of the greedy leaf splitting procedure is
Externí odkaz:
http://arxiv.org/abs/2008.05926
Autor:
Kleppe, Tore Selland
Numerical Generalized Randomized Hamiltonian Monte Carlo is introduced, as a robust, easy to use and computationally fast alternative to conventional Markov chain Monte Carlo methods for continuous target distributions. A wide class of piecewise dete
Externí odkaz:
http://arxiv.org/abs/2005.01285
We propose a state-space model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions, and adds to previous deterministic trend specificati
Externí odkaz:
http://arxiv.org/abs/2001.03984
We propose a factor state-space approach with stochastic volatility to model and forecast the term structure of future contracts on commodities. Our approach builds upon the dynamic 3-factor Nelson-Siegel model and its 4-factor Svensson extension and
Externí odkaz:
http://arxiv.org/abs/1908.07798