Zobrazeno 1 - 10
of 806
pro vyhledávání: '"62m20"'
Autor:
Kitagawa, Genshiro
This study evaluated probability distributions for modeling time series with abrupt structural changes. The Pearson type VII distribution, with an adjustable shape parameter $b$, proved versatile. The generalized Laplace distribution performed simila
Externí odkaz:
http://arxiv.org/abs/2412.07194
This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). While GPR is a competent model for learning the stochastic pat
Externí odkaz:
http://arxiv.org/abs/2412.00123
Autor:
Kitagawa, Genshiro
This paper explores a Bayesian self-organization method for state-space models, enabling simultaneous state and parameter estimation without repeated likelihood calculations. While efficient for low-dimensional models, high-dimensional cases like sea
Externí odkaz:
http://arxiv.org/abs/2411.16056
Autor:
Scholz, Eric, Weißbach, Rafael
Our model for the lifespan of an enterprise is the geometric distribution. We do not formulate a model for enterprise foundation, but assume that foundations and lifespans are independent. We aim to fit the model to information about foundation and c
Externí odkaz:
http://arxiv.org/abs/2411.12367
Autor:
Brolly, Martin T.
Stochastic parameterisations deployed in models of the Earth system frequently invoke locality assumptions such as Markovianity or spatial locality. This work highlights the impact of such assumptions on predictive performance. Both in terms of short
Externí odkaz:
http://arxiv.org/abs/2411.07041
Data assimilation (DA) combines partial observations with a dynamical model to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future
Externí odkaz:
http://arxiv.org/abs/2411.05870
Autor:
Baradel, Nicolas, Cormier, Quentin
We consider an optimal control problem inspired by neuroscience, where the dynamics is driven by a Poisson process with a controlled stochastic intensity and an uncertain parameter. Given a prior distribution for the unknown parameter, we describe it
Externí odkaz:
http://arxiv.org/abs/2411.04917
Autor:
Andreou, Marios, Chen, Nan
The Conditional Gaussian Nonlinear System (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear struct
Externí odkaz:
http://arxiv.org/abs/2410.24056
Autor:
Kassis, Georges, Macrina, Andrea
Randomized arcade processes are a class of continuous stochastic processes that interpolate in a strong sense, i.e., omega by omega, between any given ordered set of random variables, at fixed pre-specified times. Utilizing these processes as generat
Externí odkaz:
http://arxiv.org/abs/2410.16339
We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dimensional parameter sets, such as those associated to neural networks,
Externí odkaz:
http://arxiv.org/abs/2410.00620