-
Hello,
I am working on a robotics application where I am interested in learning the motion and measurement model parameters of a ground robot, and wanted to know if the the Bayesian inference metho…
-
https://github.com/JasperSnoek/spearmint
http://people.seas.harvard.edu/~jsnoek/software.html
http://fastml.com/tuning-hyperparams-automatically-with-spearmint/
http://www.johnmyleswhite.com/notebo…
-
TOA outliers (and DM outliers in wideband timing) can be robustly dealt with using methods like Huber regression. This is an alternative to running a full Bayesian analysis for outlier rejection as in…
-
Reviewer's comments:
> R2.3.2: Based on the simulated example shown in the appendix, the authors may find high reliability in recovering individual model parameters with the Bayesian approach. This…
-
There's a package called `PyMC3` that can be used for Bayesian analysis.
There's a good "hacker book" available at: https://nbviewer.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesi…
-
## What is Bayesian neural network?
- The bayesian neural network is a standard neural network with weights in a probability distribution.
## What is the difference in training?
- The learn…
-
### Description
Multivariate adaptive regression splines
### Purpose
Output similar to ordinary regression for high dimensional data. The method allows testing of non-linear associations alon…
-
I have two proposals for enhancements.
1). The first one is to include assumption checks in the Bayesian versions of all analyses. This is just a very minor issue, but it should be very easy to jus…
-
To add to FAQ. Statsmodels seems to be becoming a popular tag for pymc/bayes questions on stackoverflow.
-