Variational Bayesian State-Space Models Network
CiteSeerX — Time Series Prediction with Variational Bayesian Nonlinear State-Space Models
... the 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 ...
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Space adaptive and hierarchical Bayesian variational models for image restoration
... different space-variant regularizers, by sequentially adding a further degree of freedom in the distribution modeling the local behavior of the gradients in the ...each space-variance related to the local ...
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Efficient Bayesian estimation of multivariate state space models
... 4 Empirical Illustration 4.1 Data Description The Bayesian methodology for estimating multivariate state space models is used to analyse MODIS satellite image data. In particular, a ...
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State space reparametrization for approximating nonlinear models in Bayesian state estimation
... object state before starting the filtering process, and proper modeling of this initial distribution is essential to obtaining accurate ...the state of the target evolves through time, and models ...
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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 ...
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Bayesian estimation of state space models using moment conditions
... equilibrium models in macroeconomic applications such as dynamic stochastic general equilibrium models ...DSGE models there are alternatives to what we propose that rely on being able to solve the ...
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Auxiliary likelihood based approximate Bayesian computation in state space models
... model is likely to be tractable enough to preclude the need for treatment via ABC, with the primary goal of this paper being the presentation of ABC methods in SSMs for which exact methods are essentially infeasible. ...
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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
... for Bayesian analysis are simulation smoothing algorithms, which can be used to jointly sample the state vector of a SSM, from its full conditional posterior ...using state space ...
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Adaptive Variational Bayesian Inference for Sparse Deep Neural Network
... In this work, we investigate the theoretical aspects of variational inference for sparse DNN models. Although theoretically sound, the spike and slab modeling with Dirac spike is difficult to implement in ...
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Characterizing the Function Space for Bayesian Kernel Models
... using variational methods (Blei and Jordan, ...a Bayesian kernel model for high dimensional ...the Bayesian kernel model was used to classify gene expression data as well as digits, the MNIST ...
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A Bayesian robust Kalman smoothing framework for state space models with uncertain noise statistics
... optimal Bayesian Kalman smoother (OBKS) to obtain smoothed estimates that are optimal relative to the posterior distribution of the unknown noise ...a Bayesian innovation process and a posterior-based ...
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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 ...
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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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Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models
... nonlinear models, whose number of unknowns (the hidden-states) increases with sample size and for which no theoretical bound is ...the state-noise ...
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Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
... low/high state r): too many to be reasonably set by non-Bayesian methods like ...automatic Bayesian way. Our method is folded into the variational inference process, which lets us optimize ...
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Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models
... the state-noise precision ...and state- noise is required for stochastic dynamical systems to exhibit ‘‘interesting’’ properties, which would disappear in both low- and high-noise ...the state-noise ...
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Dynamic State Space Models
... Harrison and Stevens (1976) presents the general discussion of the Bayesian approach to state-space modeling. 6 Prediction in the frequency domain Consider the engineering context, where the ...
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MCMC for state-space models
... directly from p(θ|x 1:n , y 1:n ), or θ is of sufficiently low-dimension that we can use efficient independence proposals. In some cases we need to update components or blocks of θ at a time, rather than the updating the ...
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Bayesian Inference of State Space Models with Flexible Covariance Matrix Rank: Applications for Inflation Modeling
... (UC) models with different state correlation ...UC models with correlated or orthogonal innova- tions have been well-investigated, out-of-sample implications are less well under- ...UC models ...
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