Questions tagged [convergence]
Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity. Convergence is also a property of an iterative algorithm to stabilize on some aim value.
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Checking model after convergence issues with glmer logistic regression
I'm running a logistic regression model where subject ID is nested within litter ID, using glmer. The equation is as follows:
glmer(outcome ~ group * time + previousStatus + (1|litter/pup), data, ...
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Ascertaining sub-probabilities using re-sampling of the data. Multivariate convergence?
I am trying to compute a payout for an online game that has the following information:
40% chance of a loss with value -2
10% chance of a loss with value -1
remaining 50% chance with four potential ...
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Convergence rate of Nadaraya–Watson estimator in Holder Space
I'm currently learning non-parametric regression using some online public materials.
Specifically, consider the model
$$
y_{i} = f_{0}(x_{i}) + \epsilon_{i}
$$
where $x_{i}\in \mathcal{X} \subset \...
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Proof of Strong consistency of Beta posterior distribution
Suppose that we have random variable $X_{1}, X_{2}, ..., X_{n} \sim^{iid} \text{Bernoulli}(p_{0})$ with $p_{0}$ true unknown probability in $[0,1]$. Now, I want to implement Bayesian machinery to ...
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non-positive-definite Hessian matrix/non-convergence problem with glmmTMB
I've got a dataset that has temperature (21c or 29c), inoculation (mock (m),single inoculations (c or r), or coinoculation (rc)), and age group (y or o). I am trying to model the interactive effects ...
user26322319
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Convergence of MLE for non-IID data
Consider calculating optimal model parameter $\theta$ using MLE for the following 2 cases:
Data generating process is independent but non identical:
$L(y;\theta) = \prod_{i=1}^{n} f_{i}(y_i;\theta)$....
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If the variance converges to zero, when do we have almost sure convergence
We have that $\mathbf{E}(X_n)=c$ where c is a positive constant and $\lim_{n \rightarrow \infty} \mathtt{Var}(X_n) =0$. Then
$$ X_n \rightarrow c \quad \mbox{in probability as} \quad n \rightarrow \...
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Do convergence rates for (convex) gradient descent apply when domain is (convex) subset of reals?
I have a convex multi-variate optimization problem where each variable lies on the domain $[x, \infty)$ for some positive number $x$. I know the problem has a unique finite solution in the domain, ...
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The sum of $O_p$ --$ O_p \left(s^2\frac{\log d}{n}+s\sqrt{\frac{\log d}{n}} \right) $
I read papers in the area of inference for high-dimensional graphical models and these papers always state the convergence rate of the estimator. Using $O_p$ is a good choice.
Maybe I made some ...
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Can we have $|\overline{X}_n - E[X_1]| > \varepsilon$ infinitely often?
Let's say I have some random variables, $X_1, X_2, ...$ which are identically distributed with finite expected value, $E[X_1]$ and say they satisfy the requirements of the law of large numbers. Let
$$\...
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SVRG vs full gradient descent
Stochastic gradient descent allows us to avoid the computation of full gradients at the expense of introducing a noise floor to convergence. To decrease this noise floor, SGD requires a decrease in ...
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Doubt regarding limiting distribution on Vasicek model
I was reading an article from Vasicek where he's concerned about deriving the limiting loss probability distribution on a credit risk model with 2 factors.
I am here presenting a somewhat different ...
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Convergence of a Bayesian classifier
Background
Let $y_k$ be a noisy measurement at time $k$ and let $\{p_{k-1}(i)\}_{i=1}^n$ be (a discrete) prior probability distribution. Using Bayes rule, one can update the prior in function of $y_k$ ...
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How to approximate the point a sequence is converging to?
I have created a poker solver as part of my Master's Thesis. This solver uses Counterfactual Regret Minimization (CFR) to compute a Nash Equilibrium of Hold'em or Omaha Poker. The solver uses existing ...
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Does the mean of the maxima of a set of distributions converge?
This question is related to a recent one I posted. In that question I ask what statistic might best represent the central tendency of the true discrete distribution of a property for a sample for ...
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