Editing Student's t-distribution (section)

===Student's {{mvar|t}}&nbsp;process===
For practical [[Regression analysis|regression]] and [[prediction]] needs, Student's {{mvar|t}}&nbsp;processes were introduced, that are generalisations of the Student {{mvar|t}}&nbsp;distributions for functions. A Student's {{mvar|t}}&nbsp;process is constructed from the Student {{mvar|t}}&nbsp;distributions like a [[Gaussian process]] is constructed from the [[Multivariate normal distribution|Gaussian distributions]]. For a [[Gaussian process]], all sets of values have a multidimensional Gaussian distribution. Analogously, <math>X(t)</math> is a Student {{mvar|t}}&nbsp;process on an interval <math>I=[a,b]</math> if the correspondent values of the process <math>\ X(t_1),\ \ldots\ , X(t_n)\ </math> (<math>t_i \in I</math>) have a joint [[Multivariate t-distribution|multivariate Student {{mvar|t}}&nbsp;distribution]].<ref name="Shah2014">{{cite journal |last1= Shah| first1= Amar |last2= Wilson| first2= Andrew Gordon|last3= Ghahramani|first3= Zoubin|year= 2014 |title= Student&nbsp;{{mvar|t}} processes as alternatives to Gaussian processes|journal= JMLR|volume= 33|issue= Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS) 2014, Reykjavik, Iceland|pages= 877–885| arxiv= 1402.4306 | url= http://proceedings.mlr.press/v33/shah14.pdf}}</ref> These processes are used for regression, prediction, Bayesian optimization and related problems. For multivariate regression and multi-output prediction, the multivariate Student {{mvar|t}}&nbsp;processes are introduced and used.<ref name="Zexun2020">{{cite journal |last1= Chen| first1= Zexun |last2= Wang| first2= Bo|last3= Gorban|first3=Alexander N.|year= 2019 |title= Multivariate Gaussian and Student&nbsp;{{mvar|t}} process regression for multi-output prediction|journal= Neural Computing and Applications| volume= 32 | issue= 8 | pages= 3005–3028 |doi=10.1007/s00521-019-04687-8|doi-access= free| arxiv= 1703.04455 }}</ref>