Understanding 'Convex' & 'Concave' nature of functions

A closed-form solution of MLE exists when the parameter space is convex the likelihood function is concave.

I did not understand this statement very well. I know what 'Convex' is- a single global minimum 'Concave' is the opposite.

What does it mean when a parameter space is 'Convex'? Parameter space refers to the range of possible values a set of parameters can take. Do Convex Concave refer to the minimum maximum values of the parameter space?

Topic parameter-estimation

Category Data Science

About

Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.