MLE and Bayesian estimation method for BFGMPLx model parameters.
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MLE and Bayesian estimation method for BFGMPLx model parameters.
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2023-03-08
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Supplementary data from Sampaio Mayer et al.
mcc contains the Maximum Clade Credibility tree, while posterior_sample contains the 100 random trees from the posterior sample.
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Sir data from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models
The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have
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Posterior probability distributions (in percentages) of all 70 possible sets of the making of eight cups of tea that would all be correctly identified under three prior distribution scenarios: Pr3 represents the uniform prior distribution of three possible ability levels 0.5, 0.75, 1; Pr2 represents the uniform prior distribution of binary ability levels 0.5 or 1; Pr2a represents the uniform prior distribution of binary ability levels 0.5 or 0.75 where 0.5 means a pure guessing; 0.75 means 75% of the time the making of the tea will be correctly identified and 1 means the identification will be 100% correct.
Posterior probability distributions (in percentages) of all 70 possible sets of the making of eight cups of tea that would all be correctly identified under three prior distribution scenarios: Pr3 rep
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Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation
Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to
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Mosquito ODE system’s parameter approximation errors.
Mosquito ODE system’s parameter approximation errors.
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