Bayesian Computation via the Gibbs Sampler for Mixture Models with Gaussian Distal OutcomesLatent class analysisdistal outcomeslatent variable modelingBayesian inferenceModels with distal outcomes have been commonly used to evaluate the effect of categorical latent variables on an observed dependent variable, ...
Measurements of the gravitational constant G are notoriously difficult. Individual state-of-the-art experiments have managed to determine the value of G wi
which models Xe as a sister population of Denisovans. Model H is slightly more likely than Model F (1.2 times more likely), and much more likely than models E (5 times more likely), G (7.6 times) and D (107 times). Models not considering the presence of an extinct archaic ‘ghost’...
g-Expectations, G-expectations and model uncertainty Parametric nonlinear time series models with drift and volatility uncertainties Bayesian nonlinear expectations Estimation, forecasting and risk evaluation Applications to forecasting and risk evaluation of Bitcoin Conclusions Change history Notes References Ackno...
(e.g., charge density waves and the superconducting state) needs to be addressed via iterative synthesis and characterization of tailored materials2. Often, the rate of discovery is naturally limited by the speed at which experiments can be performed; this is particularly true for materials ...
Their computation for all pairs of variables is shown to be extremely efficient, thereby allowing us to address large problems with thousands of nodes. Moreover, we derive exact tail probabilities from the null distributions of the Bayes factors. These allow the use of any multiplicity correction ...
[42]Pfister J, Toyoizumi T, Barber D, Gerstner W (2006) Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation 18: 1318–1348. doi: 10.1162/neco.2006.18.6.1318 [43]Bi G, Poo M (1998) Synaptic modifications in cultured hippocamp...
Deviance information criteria for model selection in approximate Bayesian computation. Statistical Applications in Genetics and Molecular Biology 10:33.Franc¸ois O, Laval G: Deviance Information Criteria for Model Selection in Approximate Bayesian Computation. arXiv 2011, [http://arxiv.org/abs/...
Mongillo, G. & Deneve, S. Online learning with hidden markov models. Neural computation 20, 1706–1716 (2008). Article MathSciNet PubMed MATH Google Scholar Wilson, R. C. & Finkel, L. A neural implementation of the Kalman filter. Advances in Neural Information Processing Systems 22 (200...
Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-based approaches have appeared in the ...