Unanswered Questions
3,535 questions with no upvoted or accepted answers
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Reinforcement *Model* Learning
Classical reinforcement learning (Q- or Sarsa-Learning) can be extended with models of the environment. These models are usually transition tables that contain the probability of arriving at a ...
CommunityBot
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9
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1
answer
229
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What is Better for Prediction Error: Covariance Penalties or a Test Set?
I'm reading Computer Age Statistical Inference by Efron and Hastie, two statisticians I have a lot of respect for.
Section 12.3 discusses Mallows' $C_{p}$, Akaike's information criteria (AIC), and ...
CommunityBot
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In sports modelling, are hot simulations better or cold simulations?
I'm thinking here largely of the context in which someone has an Elo rating model for a particular sport.
To calculate things such as how often the team makes the Finals series, or wins the ...
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Are there any General Proofs on Genetic Algorithms?
Are there any general proofs or theorems relating to "genetic algorithms"?
I have been reading about a theorem in math called the "Schema Theorem" - this theorem is one of the ...
8
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1
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809
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How to predict routes using clustering data
I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
CommunityBot
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Calculate goodness-of-fit (with deviance) to compare averaged models?
I need to compare the goodness of fit of several averaged logistic regression models by calculating the deviance explained. I'm using the MuMIn package in R to ...
CommunityBot
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1
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Overfitting a neural network to a single batch as a sanity check - how small a loss value is small enough and long to run for?
I'm currently developing a neural network for a regression task. Following on from the advice given in places like here, here, and here I'm attempting to overfit my model to a single batch of 5 ...
CommunityBot
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Identifying non-linearities in relationship between variables
Logistic regression is often used to identify the effect of $x$ on a binary variable $y$ after adjusting for potential confounders $x_1,...,x_n$. In the medical literature, I will sometimes encounter ...
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What are the differences between HC estimators and their small sample properties?
I am currently using R to run regression with the following code:
...
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183
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How to combine noisy and noise-free datasets to train a model
Overview
Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
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Including feature-dependent priors on output class, in bayesian logistic regression
When doing logistic regression with data $D_N = \{(x_i, y_i)\}_i^N$ with $x_i \in \mathbf{X}^N$ (each data point has N features) and $y_i \in \mathbf{Y}$ being assigned output classes, in a Bayesian ...
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prediction interval of a new prediction out of a weighted linear regression model
This question is based on the example 11.1 out of the book Applied linear statistical models of Kutner, Nachtsheim, Neter and Li. You can find the data here.
First they calculate a simple linear ...
- 84k
7
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Graphical nominal model
Suppose I have a set of $k$ matrices.
$$
\mathbb D = A_1,A_2,...,A_k
$$
Each column of $A$ is categorical vector.
$$
A = v_1,v_2,...,v_n
$$
I want to find the mapping
$$
f: A \...
- 84k
6
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Before using CV-selected Regression model for Inference, shouldn't model performance be evaluated on unused test set?
I just came across a biokinesiology paper that used some Machine Learning methods, but I think there is a flaw in their methodology.
The authors had data on stroke patients and used Lasso regression ...
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using `lmer` to fit the linear mixed effects models
Edit: I know some people vote this question is off-topic since it is more like a Cross Validated question. However, I am not here to ask about the coding thing (but I might word in the wrong way). I ...
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