Category: BayesianPage 1 of 7
Loosely speaking, the followings are all related Fisher information matrix (FIM) is not positive definite nonidentifiable/singularities of a statistical model algebraically: map from a parameter (theta) to a…
1. Goal Here is a short SBC summary, widely used algorithm testing framework which I aim to extend as follows: An update algorithm that finds well-calibrated prior or range of well-calibration with certain…
The three main themes for my research on simulation on generative model classes are as follows with related literature: A. interaction between reality and hyperreality (simulation) on Bayesian workflow…
I am planning for three series along with the following diagram 1. Introduction to Bayesian calibration 2. Bayesian calibration meets decision and its application 3. $\text{Simulation-based calibration}^{TM}$ for…
My question began from how and where could the idea of lifting or augmentation be used cleverly like HMC? Adding the momentum variable helps structured sampling in HMC….
– How chance is solidified as probability and applied to mathematics, statistics, economics, and finance to physics and computer science – Starting from Cardano and Ramsey who thought…
Optimal exploration of model+algorithm space is my research interest. We, modelers, face this problem every day as we add, subtract, and transform the predictors to our time-series, linear…
I gave a talk on Bayesian workflow in Stan. It includes recommendations on defining “what” problems to solve and “how” to solve with one’s and community’s resources. This…
Why is divergent trasition bad? divergent transitions result in a biased sample – at the end of a divergent transition, the parameter estimates are unchanged, effectively, the same…