Marginal likelihood under the linear noise approximation
On Marginal Quasi-Likelihood Inference in Generalized Linear Mixed Models
... cally, under the assumption that the mean vector of the responses is known, these authors use the normal theory likelihood based on a first- order variance approximation to estimate the variance ...
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How reliable is the linear noise approximation of gene regulatory networks?
... the noise levels can be optimized by tuning a bimolecular rate constant whereas no such regulation is predicted by EMRE/IOS and simu- ...significantly under- estimates the amplitude and period of ...
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Bayesian inference of biochemical kinetic parameters using the linear noise approximation
... uses likelihood approximation methods ...LNA approximation provides an explicit Gaussian likeli- hood for models with hidden variables and measurement error and is therefore simpler to use and ...
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Phase diffusion and the small-noise approximation in linear amplifiers: Limitations and beyond
... for linear amplifiers that it is the Fokker–Planck equation for the Wigner function (the W ) rather than the Husimi (the Q), or the Glauber–Sudarshan (the P ) which gives the correct phase diffusion coefficient in ...
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Sharp Gaussian Approximation Bounds for Linear Systems with α-stable Noise
... on likelihood or Bayesian parameter inference for such linear models, see ...conditionally linear and Gaussian, efficient Rao- Blackwellised versions of Sequential Monte Carlo (SMC) can be applied ...
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Fitting stochastic epidemic models to gene genealogies using linear noise approximation
... with likelihood-free Bayesian ...a linear noise approximation (LNA) — a technique that allows us to approximate probability densities of stochastic epidemic model ...
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A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts
... MJP likelihood, but are numerically fragile and prohibitively expensive for com- plex ...A linear noise approximation (LNA) that replaces the MJP transition density with a Gaussian density has ...
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Empirical Likelihood for Partially Linear Models
... empirical likelihood method to partially linear ...an approximation to the residual of the model to deal with the nonparametric part so that Owen's (1990) empirical likelihood approach can be ...
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Empirical likelihood inference for linear transformation models
... (4) Under certain regularity conditions, they have shown that the solution ˆ of (3) is consistent and asymptotically normal with an explicit formula for the limiting variance–covariance matrix, which can be ...
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Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
... a linear kernel. Such models provide a likelihood-based solution but can pose significant computational ...Pairwise likelihood (PL; Renard et ...of marginal distributions of pairs of ...
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Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation Short Title: Monte Carlo Likelihood Approximation
... I started coding in the spring of 2007, first in Python and then Java. I started using R during the summer of 2007 at the Summer Institute for Training in Biostatistics, then I continued program- ming with R during my ...
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On the use of marginal posteriors in marginal likelihood estimation via importance sampling.
... univariate marginal posterior densities as the only remaining source of ...estimating marginal probabilities, the approach proposed here is particularly suited for Gibbs sampling settings where ...
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Approximation of Marginal Abatement Cost Curve
... [e Abstract Top-down models usually include piecewise-smooth functions to describe marginal cost curves, while bottom-up models describe those curves with a step function. When a bottom- up cost curve is ...
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Marginal Likelihood Estimation with the Cross Entropy Method
... More specifically, not only do existing approaches often require nontrivial programming efforts, most involve using MCMC draws to compute certain Monte Carlo averages, which are then used to derive an estimate of the ...
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On Marginal Likelihood Computation in Change-point Models
... highest marginal likelihood. When maximum likelihood estimation is feasible, one can likewise choose the model according to the Bayesian (or Schwarz) information criterion ...
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On marginal likelihood computation in change-point models
... highest marginal likelihood. When maximum likelihood estimation is feasible, one can likewise choose the model according to the Bayesian (or Schwarz) information criterion ...
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Marginal Likelihood Integrals for Mixtures of Independence Models
... of marginal likelihood integrals is central to Bayesian ...examine marginal likelihood integrals for a class of mixture models for discrete ...
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Marginal likelihood calculation for gelfand dey and Chib Method
... Electronic copy available at: http://ssrn.com/abstract=1962418 In this paper, we use a Markov Chain Monte Carol simulation to compare the per- formance of the two methods with a reduced-form volatility model as a ...
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An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
... the likelihood may not be sufficiently regular. In this case the likelihood will have a different asymptotic behavior around different points and relatively more mass of the marginal ...
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Spectral Methods for Likelihood Approximation of Spatial Processes
... c ∗ n instead of c n may make a big difference, as in the last example of Mardia and Marshall (1984). Thirdly, c ∗ n has a severe bias if some nonstationarities are present at the boundary, for instance the boundary ...
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