Posterior density
Some Likelihood Based Properties in Large Samples: Utility and Risk Aversion, Second Order Prior Selection and Posterior Density Stability
... between posterior and likelihood allows for consideration of the likelihood in relation to choosing a ...with posterior stability, matching the curvature of the log-likelihood function, the observed Fisher ...
14
Pseudo marginal Metropolis–Hastings using averages of unbiased estimators
... the posterior density is estimated using an average of unbiased estimators, such as with importance sampling, and we show that in all such cases the asymptotic variance when m samples are used is not much ...
9
Bayesian Analysis of Gamma Model with Laplace Approximation
... has been pioneered by Laplace and is known as Laplace method. [3] applied this method to approximate the ratio of two integrals involved in the posterior density. Contrary to [4], this method of summarizing ...
10
Pseudo marginal Metropolis Hastings sampling using averages of unbiased estimators
... the posterior density is estimated using an average of unbiased estimators, such as with importance sampling, and we show that in all such cases the asymptotic variance when m samples are used is not much ...
9
Bayesian analysis of marshall and olkin family of distributions
... the posterior density using both analytic and simulation method like LaplaceApproximation and Markov chain Monte Carlo (MCMC) ...the posterior results analytically and then after convergence it gives ...
8
Bayesian Estimation and Prediction for the Maxwell Failure Distribution Based on Type II Censored Data
... highest posterior density (HPD) intervals, and maximum likelihood estimators (MLEs), for the Maxwell failure distribution based on Type II censored data, ...highest posterior density ...
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Approximate Bayesian Computation in Population Genetics
... smoothly Posterior density estimation: The posterior density at but steeply to zero as |t | increases, so that few values a candidate value φ 0 for φ can be approximated using are assigned ...
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Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution
... the posterior density such that each factor corresponds to only a subset of the ...the density esti- mates for each factor, and then to estimate the full posterior density as the ...
13
7Li neutron-induced elastic scattering cross section measurement using a slowing-down spectrometer
... In a second time, the precision on the available experimental data in the C(n,el) cross section plateau is around 2%. This resolution defines the prior knowledge on this cross section. Thus, a second calculation with a ...
7
A Bayesian Hierarchical Model For Longitudinal Data
... the posterior distribution when unknown parameters occur in the prior density: empirical Bayesian analysis and hierarchical Bayesian analysis (Berger ...The posterior density is then generated ...
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Posterior Contraction Rates for Gaussian Cox Processes with Non identically Distributed Data
... of posterior density estimates given ...between density estimation and function estimation is exploited in (Belitser et ...of posterior contraction for the SGCP - (Kirichenko and Van Zanten, ...
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Threshold Extension of Gallium Arsenide/Aluminum Gallium Arsenide Terahertz Detectors and Switching in Heterostructures
... disadvantage of Bayesian method is that we have to assume the prior distribution is correct. Besides the computation of Bayesian estimate is much more complicate than that of the other two approaches—pass rate and ...
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Bayesian Parameter Estimation for the Localization of a Radioactive Source in a Heterogeneous Urban Environment.
... ∼2 m for both cases using a 30 min count time. Analysis also suggested that shorter count times could be used, with count times in excess of 450 s producing little change in the posterior density. ...
107
Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
... joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a by- ...the posterior model probability provides a convenient model ...
32
Hamiltonian Monte Carlo with Energy Conserving Subsampling
... Similarly to HMC, HMC-ECS is difficult to tune. Self-tuning algorithms such as the no- U-Turn sampler (Hoffman and Gelman, 2014) have been proposed for HMC and it would be interesting to see if our ideas can be applied ...
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Bayesian genetic analysis of milk yield in Tunisian Holstein dairy cattle population
... the existence of many (inbreeding) loops and due to the family sizes, which do not allow summing and integrat- ing out genotypes and polygenic effects from the likely- hood or posterior density. This ...
6
A Novel Voronoi Based Particle Filter for Multi Sensor Data Fusion
... proposal density based o mption that particles drawn from the likelihood function will be closer to the true posterior density in comparison to par- ticles drawn from the state transition ...
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Bayesian model based approaches in the analysis of chromatin structure and motif discovery
... results. However this problem can be circumvented by noting that the probabilities of these two extreme conditions is infinitesimally low (and typically zero in the real-life application here). However this problem can ...
146
Likelihood based inference for correlated diffusions
... conditional posterior is most of the times intractable, and the use of Metropolis steps is ...the posterior of covariance matrices, which may not necessarily be diffusion matrices, is a general MCMC issue ...
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QuestPlus: A MATLAB Implementation of the QUEST+ adaptive Psychometric Method
... the posterior requires the model to consider the likelihood of every possible response observation given every possible parameter combination and every possible combination of stimulus ...the posterior ...
6