A Sequential Monte Carlo Approach for Online Stock Market Prediction Using Hidden Markov Models [PDF]
, 2011 A sequential Monte Carlo (SMC) algorithm prediction approach is developed based on joint probability distribution in hidden Markov Models (HMM). SMC methods, a general class of Monte Carlo methods, are typically used for sampling from sequences of ...
Abass, O., Bridget, Ahani E.
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Inverse Kinematics Using Sequential Monte Carlo Methods [PDF]
, 2008 In this paper we propose an original approach to solve the Inverse Kinematics problem. Our framework is based on Sequential Monte Carlo Methods and has the advantage to avoid the classical pitfalls of numerical inversion methods since only direct calculations are required.
Courty, Nicolas, Arnaud, Élise
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Monte Carlo Methods for Rough Free Energy Landscapes: Population Annealing and Parallel Tempering
, 2011 Parallel tempering and population annealing are both effective methods for simulating equilibrium systems with rough free energy landscapes. Parallel tempering, also known as replica exchange Monte Carlo, is a Markov chain Monte Carlo method while ...
A. Schug +21 more
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A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements [PDF]
, 2019 International audienceThe first stage in any control system is to be able to accurately estimate the system's state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte
Brusey, James +4 more
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Enhanced MPPT method based on ANN-assisted sequential Monte–Carlo and quickest change detection
IET Smart Grid, 2019 The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs.
Leian Chen, Xiaodong Wang
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Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
, 2017 Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.
Lindsten, Fredrik +3 more
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Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals [PDF]
, 2017 In this article we develop a new sequential Monte Carlo (SMC) method for multilevel (ML) Monte Carlo estimation. In particular, the method can be used to estimate expectations with respect to a target probability distribution over an infinite-dimensional
Beskos, Alexandros +4 more
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A sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice core [PDF]
Nonlinear Processes in Geophysics, 2016 A technique for estimating the age–depth relationship in an ice core and evaluating its uncertainty is presented. The age–depth relationship is determined by the accumulation of snow at the site of the ice core and the thinning process as a result of the
S. Nakano +4 more
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Data-Centric Engineering, 2023 Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters.
Antonios Kamariotis +4 more
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Controlled Sequential Monte Carlo
, 2019 Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and
Bishop, Adrian N. +3 more
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