Bayesian Inference for Spatial SIS epidemic model
Bayesian nonparametric inference for stochastic epidemic models
... a Bayesian nonparametric work which further motivates ...a Bayesian nonpara- metric framework and we will consider both the case of complete observations ...the epidemic for the models from ...
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A stochastic SIS epidemic model with heterogeneous contacts.
... an epidemic in a general ...typical spatial configuration of already infected and still susceptible individuals, unlike in the integer lattice on a ...
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Bayesian Inference in Spatial Sample Selection Models
... valid inference on the entire population of fishing regions should account for this selection ...valid model of fishing productions must account for the selection problem and the spatially correlated ...
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Bayesian inference of the spatial distributions of material properties
... the spatial distributions of elastic material properties from noisy strain measurements is ...employing Bayesian inference with Nested Sampling used to explore parameter space and compute ...
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Bayesian Inference in Spatial Sample Selection Models
... of spatial correlation in the unobservables so long as data is organized by a notion of location in the relevant ...by spatial flexibility of a job seeker in combination with the spatial ...
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Online Bayesian phylodynamic inference in BEAST with application to epidemic reconstruction
... online inference framework which augments a previous analysis with newly obtained data (see Materials and Methods), and a de novo analysis using a randomly generated starting tree and default starting values for ...
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A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
... Approximate Bayesian Computation (ABC) allows us to perform inference without having to compute the ...stochastic epidemic model is relatively ...make inference for the parameters of ...
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Bayesian inference for indirectly observed stochastic processes, applications to epidemic modelling
... Stochastic processes are mathematical objects that offer a probabilistic representation of how some quantities evolve in time. In this thesis we focus on estimating the trajectory and parameters of dynamical systems in ...
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Bayesian inference for the dissimilarity index in the presence of spatial autocorrelation
... statistical inference and should be routinely constructed for segregation measures, which at present they are ...the spatial autocorrelation present in the data, rather than using simple method of moments ...
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Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models
... The model has an average run time of ≈5–10 mins per simulation (in the well-supported region—it can be far longer [>3 hours] in some areas of poor ...the model fit was ...
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Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
... We introduced these aspects by implementing the Cauchemez–Ferguson (CF) method with a non- parametrically modeled infection rate with a GP prior distribution. Specifically, we assume that the infec- tion rate is constant ...
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Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models
... the model and priors can be found in Andrianakis et al. [ 2015 ]. The model has an average run time of ≈5–10 mins per simulation (in the well-supported region—it can be far longer [>3 hours] in some ...
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The SIS epidemic model with Markovian switching
... stochastic SIS model and also established conditions for extinction and persistence of the ...switching model a threshold value T 0 S was defined for almost sure persistence or ...
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Analysis of an SIS epidemic model with treatment
... Abstract An SIS epidemic model with saturated incidence rate and treatment is considered. According to different recovery rates, we use differential stability theory and qualitative theory to analyze ...
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Parameter estimation for the stochastic SIS epidemic model
... 4 Summary and Further Work In this paper we have applied the pseudo-MLE and the least squares method to estimate the parameters in the stochastic SIS model. For the least squares method, we started with the ...
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Outbreak analysis of an SIS epidemic model with rewiring
... an SIS epidemic model defined on networks. The model, introduced by Gross, D’Lima and Blasius in 2006, is based on the pair-approximation formalism and assumes that, at a given rewiring rate, ...
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A stochastic differential equation SIS epidemic model
... possible epidemic models and because it is so simple it and its variants are commonly ...example, SIS models are discussed by Brauer et ...of SIS epidemic models in the ...
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Demographic stochasticity in the SDE SIS epidemic model
... the SIS epidemic model, for example Hethcote [17] studied the SIS epidemic model involving different factors such as disease mortality and ...The SIS epidemic ...
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Complex dynamics in an SIS epidemic model with nonlinear incidence
... an epidemic model with nonlinear incidence rate, describing the saturated mass action and the psychological effect of certain serious diseases on the ...
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The asymptotic behavior of a stochastic SIS epidemic model with vaccination
... simple epidemic model, there is generally a threshold, R ...of epidemic model and it is globally asymptot- ically stable; if R > , this type of model has also a unique endemic ...
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