With braiding
With duals for objects
category with duals (list of them)
dualizable object (what they have)
ribbon category, a.k.a. tortile category
With duals for morphisms
monoidal dagger-category?
With traces
Closed structure
Special sorts of products
Semisimplicity
Morphisms
Internal monoids
Examples
Theorems
In higher category theory
(…)
(…)
Let $(\Omega,\mathcal{F},p)$ be a probability space, and let $A,B\in\mathcal{F}$ be events, i.e. measurable subsets of $\Omega$. We say that $A$ and $B$ are independent if and only if
i.e. if the joint probability is the product of the probabilities.
More generally, if $f:(\Omega,\mathcal{F})\to(X,\mathcal{A})$ and $g:(\Omega,\mathcal{F})\to(Y,\mathcal{B})$ are random variables or random elements, one says that $f$ and $g$ are independent if and only if all the events they induce are independent, i.e. for every $A\in\mathcal{A}$ and $B\in\mathcal{B}$,
Equivalently, one can form the joint random variable $(f,g):(\Omega,\mathcal{F})\to(X\times Y,\mathcal{A}\otimes\mathcal{B})$ and form the joint distribution $q=(f,g)_*p$ on $X\times Y$. We have that $f$ and $g$ are independent as random variables if and only if for every $A\in\mathcal{A}$ and $B\in\mathcal{B}$,
If we denote the marginal distributions of $q$ by $q_X$ and $q_Y$, the independence condition reads
meaning that the joint distribution $q$ is the product of its marginals.
(…)
(…)
(…)
(For now see Markov category - Stochastic independence)
Kenta Cho, Bart Jacobs, Disintegration and Bayesian Inversion via String Diagrams, Mathematical Structures of Computer Science 29, 2019. (arXiv:1709.00322)
Tobias Fritz, A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics, Advances of Mathematics 370, 2020. (arXiv:1908.07021)
Last revised on February 22, 2024 at 16:11:37. See the history of this page for a list of all contributions to it.