Inference Algorithm

Context:
- It can range from being a Logical Inference Algorithm(Exact Inference Learning?) to being a Statistical Inference Algorithm, such as a Bayesian Inference Algorithm.
Example(s):
- a Junction Tree Algorithm,
- an MCMC Algorithm,
- a Markov Logic Inference Algorithm,
- …
Counter-Example(s):
- a Model Training Algorithm.
See: Statistical Inference, Variational Inference Algorithm.

References

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Algorithmic_inference Retrieved:2017-9-9.
- Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural probability .
  The main focus is on the algorithms which compute statistics rooting the study of a random phenomenon, along with the amount of data they must feed on to produce reliable results. This shifts the interest of mathematicians from the study of the distribution laws to the functional properties of the statistics, and the interest of computer scientists from the algorithms for processing data to the information they process.

(Paskin, 2015) ⇒ Mark Paskin (2015)."A Short Course on Graphical Models:3. The Junction Tree Algorithms".
- QUOTE: The junction tree algorithms take as input a decomposable density and its junction tree. They have the same distributed structure:
  - Each cluster starts out knowing only its local potential and its neighbors.
  - Each cluster sends one message (potential function) to each neighbor.
  - By combining its local potential with the messages it receives, each cluster is able to compute the marginal density of its variables.