In mathematics it happens at times that one and the same concept is given two different names to indicate a specific perspective, a certain attitude as to what to do with such objects.
But one says series instead of sequence when one is interested in studying its partial sums. In particular it means something different to say that a series converges than to say that a sequence converges. The series $n\mapsto a_n$ converges if and only if the sequence $n\mapsto \sum_{i\lt n}a_i$ converges.
A module object in a monoidal category $C$ is the same as an action object in $C$…
A presheaf is just a contravariant functor, just as a copresheaf is just a functor.
(More specifically, an “$S$-valued presheaf” is a contravariant functor with codomain a given category $S$; in modern category theory the “default” value of $S$ for a presheaf is usually Set.)
But one says presheaf instead of (set-valued) contravariant functor when one is interested in studying its sheafification, or even if one is just interested in regarding the category of functors with its structure of a topos: the presheaf topos.
A Young diagram is a partition that wants to become a Young tableau.
A quiver is just a directed graph (pseudograph, to be explicit).
But one says quiver instead of directed graph when one is interested in studying quiver representations: functors from the free category on that graph to the category of finite-dimensional vector spaces.
A field (in physics) is just a section of a fiber bundle.
But in mathematical physics one says field instead of section of a fiber bundle to indicate that one is going to consider a Lagrangian density on the corresponding jet bundle of the given fiber bundle (then called the field bundle ) and study the induced classical or quantum field theory.
Both random variables and estimators are almost always just real valued measurable maps. Though sometimes the former takes more general values in some Polish space instead.
But in probability theory a random variable is interpreted as a map from a sample space to a space of states which represents the observed outcomes or outcomes predicted by some model. In contrast in statistics an estimator guesses or estimates a certain parameter associated to some random model.
A tensor network is just a string diagram (i.e. Penrose notation).
But in discussion of the AdS/CFT correspondence in solid state physics one says tensor network when regarding string diagrams as encoding quantum states in the Hilbert space which is the tensor product of all the external vertices.
Last revised on May 19, 2021 at 20:01:04. See the history of this page for a list of all contributions to it.