Skip to main content
Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Code code:"if (foo != bar)"
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Saves in:saves
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results tagged with
Search options not deleted user 317937

Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.

0 votes

Submartingales bounded in $L^p$, $p>1$

A stronger condition that does guarantee $L^p$ convergence for submartingales is as follows: $\sup_k \{E|M_k|^p + E|A_k|^p\} < \infty.$ where $X_k = M_k + A_k$ is Doob's decomposition. For a proof, …
Lars's user avatar
  • 625
1 vote

$L^p$-convergence of submartingale

Here is one alternative set of conditions (sufficient but I am not sure if they are necessary). Essentially, I move the $L^p$-uniform integrability requirement of $X_k$ to the predictable component $A …
Lars's user avatar
  • 625
1 vote
Accepted

Covering number of the conditional distribution function

You need a different approach. Each function in your function space can be written as $$F_{Y|W}(y|W) = \int 1(s \leq y) P(Y = ds|W)$$ for some $y$. Thus, $$\|F_{Y|W}(y_2|W) - F_{Y|W}(y_1|W)\|_{L^1} = …
Lars's user avatar
  • 625