.png){kind=icon}
Return
Estimation and Inference by Compact Coding
.png){kind=icon}
PRE
AI
DOI：10.1111/j.2517-6161.1987.tb01695.x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
SUMMARY
The systematic variation within a set of data, as represented by a usual statistical model, may be used to encode the data in a more compact form than would be possible if they were considered to be purely random. The encoded form has two parts. The first states the inferred estimates of the unknown parameters in the model, the second states the data using an optimal code based on the data probability distribution implied by those parameter estimates. Choosing the model and the estimates that give the most compact coding leads to an interesting general inference procedure. In its strict form it has great generality and several nice properties but is computationally infeasible. An approximate form is developed and its relation to other methods is explored.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
No journal information available
Organization
No organization information available
Cited Papers
No cited papers available