MCMC estimation and L´evy type models
Inference on Phase-type Models via MCMC
... Intro Phase-type Distributions Bayesian Inference for PHT Computational Issues Network Inference The Big Issue Intractable computation time for many applications! 1 longer chains and MCMC jumps to states ...
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MCMC, likelihood estimation and identifiability problems in DLM models
... Bayesian estimation procedure based on MCMC techniques, we can identify the troublesome parameters and deal with unidentified models using convenient informative priors or any other suitable ...with ...
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Parameter Estimation for Discretely Observed Vasicek Model Driven by Small L´evy Noises
... perturbation. L´evy noise, as a kind of important non-Gaussian noise, has attracted wide attention in the research and practice in the fields of engineering, economy and ...asymptotic estimation for ...
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MCMC for inference on phase type and masked system lifetime models
... By way of comparison, the starting point was exactly the same model and data as used in both § 4.1.1 and § 4.3.1. The 3rd quartile time was then used as a censoring time to emulate an experiment in which the longest ...
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Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models
... two models considered, and for the empirically relevant parameter ranges explored, gains in simulation efficiency are produced by moving either the location or scale parameter from the state equation to the ...
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Type Based MCMC
... variable models—such as EM and existing Gibbs samplers—are token-based, meaning that they update the variables associated with one sentence at a ...a type-based sampler, which up- dates a block of ...
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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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Epidemic models and MCMC inference
... π(X 0 )q(x t−1 |X 0 ) π(x t−1 )q(X 0 |x t−1 ) (3.5.8) which is the same as equation 3.5.2. However this generates a Markov Chain on X with an unknown invariant distribution, whose bias is unknown both in size and ...
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Phase-type inference on competing risks models with covariates, using MCMC methods
... In fact, experience shows that the total run time of the algorithm becomes smaller with a very low number of Metropolis-Hastings iterations for each Gibbs-step. This has been the strategy while sampling the results in ...
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Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
... volatility models using MCMC methods highly depends on actual parameter values in terms of sampling ...volatility models in order to greatly improve sampling efficiency for all parameters and ...
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Inference in MCMC step selection models
... likelihood estimation can be used for simultaneous inference about movement and habitat ...rejection-free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive ...
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Bayesian Model Selection And Estimation Without Mcmc
... And Estimation Without Mcmc Abstract This dissertation explores Bayesian model selection and estimation in settings where the model space is too vast to rely on Markov Chain Monte Carlo for posterior ...
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Estimation of Hyperbolic Diffusion Using MCMC Method
... of MCMC simulation for Bayesian ...nonlinear models with latent variables and in dynamic models that are nearly nonstationary and ...The MCMC strategy has also been widely used in ...
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A Generalized Linear Transformation Method for Simulating Meixner L´evy Processes
... the models and the complexity of the financial products also imply that only in rare cases there exists tractable pricing formulaes for these prod- ...binomial models, finite difference methods, Monte Carlo ...
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On Sequential Calibration for an Asset Price Model with Piecewise L´evy Processes
... piecewise L´evy ...well-known models are the Heston model [10] and the SABR model [9], both of which are of stochastic volatility ...those models, the plain vanilla premium or the implied ...
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MCMC for normalized random measure mixture models
... Abstract. This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture mod- els with normalized random measure priors. Making use of some recent posterior ...
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On the valuation ofconstant barrier options under spectrally one-sided exponential L&evy models and Carr’s approximation for American puts.
... where ˜!(y) = !(y) − ˜ f(y). In the 9nal approximation we would replace back the “Canadian European” value ˜ f(y) by the exact European value, and add to it the approximate early exercise premium ˜V: This idea has ...
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Efficient Bayesian estimation and combination of GARCH type models
... reports estimation results for importance sampling using a naive importance density, ...both models, as compared with ...for L = 100 000 draws, the naive ap- proach may hardly cover some relevant ...
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Efficient Bayesian Estimation and Combination of GARCH-Type Models
... ML estimation of GARCH-type ...GARCH models, the interest usually does not center directly on the model parameters but on possi- bly complicated nonlinear functions of the ...GARCH-type ...
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Stochastic Gradient MCMC for Nonlinear State Space Models
... Christopher Nemeth, Paul Fearnhead, and Lyudmila Mihaylova. Particle approximations of the score and observed information matrix for parameter estimation in state–space models with linear computational ...
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