Do you want BuboFlash to help you learning these things? Or do you want to add or correct something? Click here to log in or create user.

#bayesianism #cognitive-science #computation #computational-psychology
he focus of this chapter will be on problems in higher-level cognition: inferring causal structure from patterns of statistical correlation, learning about categories and hidden properties of objects, and learning the meanings of words. This focus is partly a pragmatic choice, as these topics are the sub- ject of our own research and hence we know them best. But there are also deeper rea- sons for this choice. Learning about causal relations, category structures, or the proper- ties or names of objects are problems that are very close to the classic problems of induction that have been much discussed and puzzled over in the Western philo- sophical tradition. Showing how Bayesian methods can apply to these problems thus illustrates clearly their importance in un- derstanding phenomena of induction more generally. These are also cases where the im- portant mathematical principles and tech- niques of Bayesian statistics can be applied in a relatively straightforward way. They thus provide an ideal training ground for readers new to Bayesian modeling.
If you want to change selection, open document below and click on "Move attachment"


owner: rappatoni - (no access) - [Ron_Sun]_The_Cambridge_Handbook_of_Computational.pdf, p75


statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on



Do you want to join discussion? Click here to log in or create user.