Category: BayesianPage 3 of 7
I wish to solve multimodal distribution optimization in two stage approach. Example usecase is newsvendor problem; order quantity should be optimized for the multimodal-shaped demand due to its…
no theoretical guarantee for accurate results (Wang18) marginal variances of the parameters are often underestimated (Turner11) Turner11 introduce two problems in applying vEM to time series compactness– separated…
Applying the advantage of Bayesian paradigm(hierarchical modeling, coherent treatment of uncertainty) to big data setting is active in variational Bayes (VB). In VB, marginal likelihood’s variational lower bound…
reference
Three criteria are reviewed. KL divergence -divergence Wasserstein distance bound the difference between expectation of any smooth function. With upper bounds on the function of interest and the…
Computing the posterior is our target. Expectation to get the marginal as we might not know the exact data distribution. Even if we do, computation is burdensome in…
Bayesian inference involves three steps that go beyond classical estimation. 1. data and model are combined to form a posterior distribution, which we typically summarize by a set…
motivation: variability . and heavy computation of point-wise-hyperparmeter SBC check approximate the whole procedure using sensitivity to alpha and may work
Use cases: map, mle estimators with gradient descent or quasi Newton methods (1) posterior sampling with HMC (log density, 1 for Euclidean, 3 for Riemannian) standard error, posterior…