5 papers
Numerical Generalized Randomized HMC processes for restricted domains
Tore Selland Kleppe, Roman Liesenfeld
We propose a generic approach for numerically efficient simulation from analytically intractable distributions with constrained support. Our approach relies upon Generalized Random…
Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices
Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld +1
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 s…
Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility
Tore Selland Kleppe, Roman Liesenfeld, Guilherme Valle Moura +1
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 dynami…
Importance Sampling-based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models
Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld
We propose an importance sampling (IS)-based transport map Hamiltonian Monte Carlo procedure for performing full Bayesian analysis in general nonlinear high-dimensional hierarchica…
The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Oliver Grothe, Tore Selland Kleppe, Roman Liesenfeld
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedur…