parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide...
data-sciencedata-analysisbayesian-inferencebayesian-statistics UpdatedJun 4, 2024 Jupyter Notebook JavierAntoran/Bayesian-Neural-Networks Star1.8k Code Issues Pull requests Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more ...
Variational Inference (VI) MCMC 的计算复杂度比较高,序列收敛的时间更长,但是 MCMC 本质上是一个渐进无偏估计 (asymptotically unbiased estimation),所以相对于 VI,MCMC 的精度更高。VI 使用一个简单分布拟合复杂的分布,必然会引入 bias,但是 VI 的效率很高,适合用在大规模计算中(比如 VAE,Variational AutoEncoder...
Bayesian inferenceA finite element (FE) model is developed for a curved cable-stayed footbridge located in Terni (Umbria Region, Central Italy) which accounts for uncertainties in geometry, material properties, and boundary conditions as well as limited knowledge on the behavior of connections and ...
Overall, the neural implementation of inference and choice in our POMDP framework is both simple and plausible. Results We developed and tested our model using behavioral data from monkeys performing a direction discrimination task with post-decision wagering (Fig. 1a)2. On each trial, monkeys ...
Bayesian inference and life testing plans for generalized exponential distribution Recently generalized exponential distribution has received considerable attentions. In this paper, we deal with the Bayesian inference of the unknown param... D Kundu,B Pradhan - 《Science in China》 被引量: 79发表: 20...
Bayesian inference Gibbs sampling breast cancer Author Information Show + 1. Introduction The era of “big data” has arrived to the field of computational biology [1]. Biological systems are so complex that in many situations, it is not feasible to directly measure the target signals. Actually...
Bayesian inference : with ecological applications / This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environme... BARKER,J Ric...
Bayesian inference for logistic models using Polya-Gamma latent variables We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Polya-... NG Polson,JG Scott,J Windle - 《Journal of the American ...
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition...