Bayesian state-space model
Bayesian state-space model of fin whale abundance trends from a 1991–2008 time series of line-transect surveys in the California Current
... trend model (effectively, fewer parameters are estimated) with a joint posterior distribution in which multiple data sets (in our case counts, group sizes and detection distances) all influence the estimates of ...
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Bayesian Prediction of Bi Component State Space Model to Chinese Population
... bi-component state space model for predicting population reasonably and effectively, thereby reducing resource waste, and adhering to the path of sustainable ...
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Efficient Bayesian estimation of multivariate state space models
... and simulation smoothing presented in Section 3.1.2 over that of the stan- dard simulation smoother approach are presented. It is interesting that a small gain in computational e¢ ciency is made when p = 1. Examination ...
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State space reparametrization for approximating nonlinear models in Bayesian state estimation
... measurement model, or the likelihood of observing measurement z k conditioned on state ...measurement model to integrate the information of any acquired measurements into the filtering ...object ...
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Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery
... Knowing the status of marine fish stock is of utmost importance to develop management strategies for sustainable harvest of marine fishery resources. A widely accepted approach towards this is to derive sustainable ...
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Approximate Bayesian inference methods for stochastic state space models
... in state-space ...linear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem with ...
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Bayesian estimation of state space models using moment conditions
... the model numer- ...the model, use the approximation to obtain an analytical expression for the measurement density, and then use some method of numerical integration such as particle filtering to eliminate ...
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Auxiliary likelihood based approximate Bayesian computation in state space models
... auxiliary model conflicts with the quest for an accurate non-parametric estimate of the posterior using the ABC draws, given that the dimension of the parameter set in the auxiliary model is, by ...
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CiteSeerX — Time Series Prediction with Variational Bayesian Nonlinear State-Space Models
... variational Bayesian method for learning nonlinear state-space models introduced by Valpola and Karhunen in 2002 is applied to pre- diction in the ESTSP’07 time series prediction competition data ...
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The Efficient Particle MCMC Algorithms for Bayesian Estimation of Nonlinear State Space Models
... Firstly, the thesis proposes the multiple-try particle MH algorithm. The new al- gorithm accelerates the convergence of particle MH chain by multiple-try strategy. Through appropriately use of nonlinear Kalman filter for ...
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PySSM: A python module for Bayesian inference of linear Gaussian state space models
... The code above initialises the required system matrices. Note that, as we use diffuse initial conditions and a1 is only a dummy argument in this case. Further, setting wmat to zeros is required here as diffuse initial ...
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Parameter and state model reduction for Bayesian statistical inverse problems
... forward model. In Section 2.1, we outline the properties of the forward model corresponding to the steady groundwater flow ...forward model is established, the inverse problem is formulated in two ...
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A Bayesian robust Kalman smoothing framework for state space models with uncertain noise statistics
... Fig. 6 Average middle-point MSE of different Kalman smoothers for the target tracking example with unknown r and q. The average performance of each model-specific Kalman smoother corresponding to the noise ...
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Coupling stochastic EM and Approximate Bayesian computation for parameter inference in state-space models
... pseudo-marginal Bayesian algo- rithm We compare the results above with the iterated filtering methodology for maximum likelihood estimation (IF2, Ionides et ...systems state (using the bootstrap ...forward ...
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MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
... Nonlinear state-space models are ubiquitous in model- ing real-world dynamical ...of state-space ...parameter space, which makes their computation intractable for large systems ...
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Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market
... two Bayesian algorithms to efficiently estimate non-linear/non-Gaussian switch- ing state space models by extending a standard Particle Markov chain Monte Carlo (PMCMC) ...proposed Bayesian ...
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Bayesian stable mixture model of state densities of generalized Chua's circuit
... to model the state PDFs of ...the state space model of GCC is known then we can a priori determine the number of clusters which might be greater than the num- ber of scrolls for GCC ...
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Bayesian Inference in a Non linear/Non Gaussian Switching State Space Model: Regime dependent Leverage Effect in the U S Stock Market
... for Bayesian inference of NLG-SSSMs based on the results presented in section 2, convergence of their sampler may be very slow especially without a large number of particles 2 ...generating model ...
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Bayesian Inference of State Space Models with Flexible Covariance Matrix Rank: Applications for Inflation Modeling
... the model, thus, suggesting that the Gaussian proposal well approximates the conditional posterior density of θ − σ 2 | e y, z, σ 2 ...each model takes less than 180 ...
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A semiparametric state space model
... the state and observation equations and, most critically, employing a Gaussian kernel inside the observation noise density estima- tor, the observation noise sequence can be estimated from the data by standard ...
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