Keywords
Related Publications
Gaussian Processes for Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over...
Inference from Iterative Simulation Using Multiple Sequences
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, ho...
Practical Bayesian Density Estimation Using Mixtures of Normals
Abstract Mixtures of normals provide a flexible model for estimating densities in a Bayesian framework. There are some difficulties with this model, however. First, standard ref...
PyMC: Bayesian Stochastic Modelling in Python.
This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain...
Publication Info
- Year
- 1970
- Type
- article
- Volume
- 33
- Pages
- 207-232
- Citations
- 60
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1016/0001-6918(70)90134-4