Questions tagged [references]

Questions seeking external references (books, papers, etc.) about a particular subject. Always use a more specific tag in addition.

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Textbook on high-dimensional statistics

I am a beginning PhD student in biostatistics and want to learn about high-dimensional statistics. I have looked into the books by Buehlmann/Geer, Wainwright, and Giraud, but they seem to be targeted ...
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Compare Probabilistic Graphical Models and Probabilistic Reasoning In Intelligent Systems

I am new to probabilistic graphical models. I am currently reading Probabilistic Reasoning In Intelligent Systems by Judea Pearl. I found the first two chapters (and first half of third chapter, till ...
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1 answer
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Resources to understand the bootstrap

I am looking for any recommendations on resources to learn more about bootstrapping from a theoretical and rigorous perspective in terms of bounds/guarantees/etc. Any books or papers would be very ...
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Real-time forecast combination/blending with missing models

I have a time series forecasting model that runs every 15 mins and blends forecasts from 5 different models using a regression-based combination model. The weights of the combination model are updated ...
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References on the likelihood-ratio, score, and wald-tests with the most examples of solved problems?

What books discuss the likelihood-ratio, score, and wald-tests with the most examples on solving these problems?
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1 vote
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Looking for introductory materials on energy-based models and deep learning

I'm looking for introductory materials on energy-based learning, especially in its intersection with deep learning methods. Accompanying sample implementations using recent deep learning frameworks ...
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1 answer
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Research methods book for PhD students [closed]

Would it be possible to get recommendations for advanced research methods( general) research methods of Psychology for PhD students?
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1 answer
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Parameters of the Nakagami Distribution given a known Gamma distribution

Hopefully an easy one. My apologies for not using math format, I am not sure how to do so. I have a known Gamma distribution f(Y), say shape=3 and scale=2. I also know that that the distribution of f(...
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5 votes
2 answers
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Is there a name for a logical fallacy that uses irrelevant or unfamiliar statistics to make a point?

I think there is a dishonest or fallacious argument based on quoting some fact or figure or statistic that most people do not know, and cannot put into context, and then pretending that it is serious ...
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1 vote
0 answers
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Solutions to 'Statistical Learning with Sparsity'

I've recently been working through Statistical Learning with Sparsity (SLS) by Hastie, Tibshirani and Hastie. I found some exercises very hard, and think I found some mistakes. A set of solutions ...
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Expected Card Advantage in YGO - Going beyond the multivariate hypergeometric formula

Repost from MSE. I struggle to get any response to this on MSE and on MO. If you like you can recast everything below as a problem about drawing $k$ balls of $M$ different colors from an urn without ...
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1 vote
1 answer
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Reference for Logistic and Sigmoid Kernels

I've been close reading a paper and was mystified when the author mentioned two kernels: The logistic kernel $\frac{1}{e^x + 2 + e^{-x}}$ and the sigmoid kernel $\frac{2}{\pi}\frac{1}{e^x + e^{-x}}$. ...
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Is Probabilistic Reasoning In Intelligent Systems a pre-requisite for Causality?

After having read Book Of Why and Causal Inference In Statistics - A Primer, I was reading Causality - all by Judea Pearl. Yet I found that there were quite some points which I was not understanding. ...
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3 votes
1 answer
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Resources for learning about lasso

I'm looking for resources on lasso regression at the undergraduate mathematics level. All I can find is a lot of complex texts on variable selection, concentration inequalities, etc. I would like to ...
1 vote
1 answer
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Can neural networks be categorized via set inclusion or ordering?

If we were to consider the classes of: linear regression polynomial regression logistic regression softmax regresson mlp conv net recurrent net residual net... Does there exist categorization of ...
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What are the deep learning books covering stochastic differential equations only?

I want to solve a simple Stochastic Differential Equation say $$dY=Y^2 dt+\sigma Y^2 dW$$ and then make future predictions. I am conversant with MATLAB and LSTMs in python. Is there a book that can ...
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References for the conjugate prior to the beta distribution?

The Wikipedia article about "Conjugate Prior" has a table containing information about Likelihood Distributions with their Conjugate Priors. In the "Continuous Likelihood" table, ...
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Is it efficient to implement binarized neural networks on hardware rather than other types?

I'm digging into the topic of binarized / binary neural networks (BNN). One of my questions I have is, whether there exists hardware which accelerates the BNN execution. (1) The other question is, ...
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Soft Question: Resources for infinite dimensional Bayesian statistics

I'm looking to learn about infinite dimensional Bayesian statistics and was wondering if there are any good introductory references I could be pointed to. I've been struggling to find a good ...
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Applied Bayesian Statistics - Textbooks

I understand the bayesian statistics, but I don't see many textbooks approaching the bayesian statistics in a practical way. Some book recommendations on Bayesian Practical Applications would be great....
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Recommend a book on Bayesian Hierarchical Modeling

I would like to learn about Bayesian Hierarchical Modeling and its applications to time series analysis. I would appreciate a good book / publication / course which will both teach some fundamental ...
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Is there literature about how different neural network layers recognise certain features?

Let's consider a deep convolutional network. It seems that there is some consensus on the following notions: 1. Shallow layers tend to recognise more low-level features such as edges and curves 2. ...
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3 votes
1 answer
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Methods for drawing population inferences from multiple sub-population datasets

What would be an appropriate model or method for making inferences about a broader population quantity from multiple quantities representing subsets of the population? Imagine, as an example, that I ...
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2 votes
4 answers
50 views

good intermediate-level textbook for undergraduate applied statistics in data science?

I will be teaching an applied statistics course for the first time and the main audience will be 2nd and 3rd year undergraduates, mostly data science majors. They will have an intro statistics course ...
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Bayesian book not outdated [duplicate]

I did an introductory course to Bayesian in my master's degree in Statistics. I did not understand much since it was too much in short time, very concentrated. We have covered from the most basic (...
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Logistic regression with GAM smoothing

I'm working through an example of using cubic spline regression for logistic regression classification from Elements of Statistical Learning [1] (Phoneme classification - Example 5.2.3 on page 148). ...
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3 votes
2 answers
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What to do if you Double count in experimental design?

I have to run A/B/n tests for a subscription service. Generally computing metrics for this situtation is ok: For example, coversion rate experiments. We have 1000 prospects in a group, and (say) 89 ...
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1 vote
1 answer
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What is a name of regression?

Let's say there is a set of independent variables $x_1, x_2, ..., x_n$ and a target variable $y$. A transformation is applied to the initial set of variables: $z_1=f_1(x_1,x_2,..., x_k)$, $z_2=f_2(x_{...
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Deviation estimated by humans

I'd like to know which type of deviation humans will naturally estimate if asked to. Like, if you give them some points on a line and ask them to estimate the "plus or minus error" around a ...
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Adding noise to a dataset to "hide" its distribution

Given a dataset that follows "some"[1] distribution, how can we add noise to the dataset, such that when the original dataset plus the noise is shown to someone, it looks uniformly random[2]?...
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2 votes
2 answers
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Parameters estimations with right censored data

As I was doing some research about survival analysis, I found the following article stating that, if we have right censored data, we can estimate the parameters of a distribution with the following ...
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1 vote
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What should I study after finishing 'Causal Inference in Statistics: A Primer'?

I have almost finished studying 'Causal Inference in Statistics: A Primer', but I still feel that I need to learn more. I considered 'Causality' (Pearl, 2009), but there seem to be several good ...
1 vote
0 answers
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References for BERT training from scratch

As the title of the question suggests, I'm interested in training a BERT-like model (and then use it to make some experiments on text-similarity). Question: Could you share some references on the ...
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9 votes
2 answers
669 views

What are the k-means algorithm assumptions?

I'm trying to understand what are the assumptions/hypothesis underlying the k-means clustering algorythm; specifically, I'm looking for a research/academic paper listing such hypothesis and explaining ...
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1 vote
0 answers
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Limit distribution of the joint distribution of maximum and minimum of a sequence of random variables

Assume we have a sequence $\mathsf{X}_1,\mathsf{X}_2,\mathsf{X}_3,...$ of iid random variables. Then the Fisher-Tippet-Gnedenko theorem shows that $$ \mathbb{P}\left(\frac{\max\{\mathsf{X}_1,\mathsf{X}...
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8 votes
2 answers
284 views

Objective criteria for assumption violations that do not utilize p-values?

Suppose we have a standard regression model and want to identify whether we have violated the assumptions of the model. Traditionally, we might utilize a significance test to determine whether (for ...
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0 votes
1 answer
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References for cross-validation implementations in Pytorch

I'm interested in good references on cross-validation implementations for feed-forward neural networks in pytorch from scratch. Thanks in advance.
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2 votes
1 answer
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Is there a statistic such that for large sample sizes $a_n (\hat{\theta} - \theta) \sim N(0, \Sigma)$ approximately but $a_n \neq n^{1/2}$?

Various central limit theorems are of the form $a_n(\hat{\theta}-\theta)\sim N(0, \Sigma)$ approximately as $n \to \infty$ and usually $a_n = n^{1/2}$. Are there central limit theorems for statistics ...
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0 answers
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Does Geyer's mode selection heuristic perform no-worse than burn-in?

In Charles Geyer's Burn-In is Unnecessary he writes Another possible rule is to start at a point, like the mode, known to have reasonably high probability. If no such point is known, this rule is ...
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1 vote
1 answer
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Formal approach to message passing algorithms

I'm trying to understand message passing algorithms, especially for the specific application of performing conditioning in a Bayesian network. My question is wether there is a mathematically precise ...
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0 answers
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Book recommdations for reinforcement learning [duplicate]

I recently read Learning from data by Abu Mostafa. Can someone recommend a book focused on reinforcement learning which is just as mathematically rigorous as learning from data?
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2 votes
1 answer
60 views

Proof that a necessary condition for characteristic roots to lie inside unit circle is $\sum\limits_{i=1}^{n} a_{1} < 1$

I have been trying to show that given $$P_{n}(\alpha) = \alpha^{n} - a_{1}\alpha_{n-1} - a_{2}\alpha^{n-2}... - a_{n} = 0,$$ the $\alpha$'s that solve this equation (real-valued or complex) lie in the ...
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1 vote
0 answers
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What is a good introductory guide to Bayesian MCMC Analysis in R?

I am trying to perform Bayesian Analysis in R, using of Monte Carlo Markov Chains to calculate the probability of, in a set of data, there being a gaussian peak at a certain location $x_0$ (my 'target'...
29 votes
4 answers
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What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?

I read not too long ago Nelder and McCullagh's book Generalized Linear Models and thought the book was fantastic and I consider it a useful manual on the subject. Not surprising that's the case, ...
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Source for KL-divergence of Beta distribution?

This post explains how to derive the Kullback-Leibler divergence between two beta distributions. https://math.stackexchange.com/questions/257821/kullback-liebler-divergence#comment564291_257821 I ...
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1 vote
1 answer
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How to tell if my features improve model performance?

Setup Task: binary classification Models: logistic regression, SVM, ELM, neural networks - anything that can do classification Dataset: 10 basic features + 6 my own features Question How do I see ...
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How to interpret the coefficients in robust weighted Tobit regression model?

I am looking for any reference helping explaining the coefficients in Tobit model for beginner. I found a topic in our exchange but it seems still not clear to me. I am finding a handy reference like ...
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1 vote
1 answer
25 views

3D convolutions and jitter

I want to process a sequence of cropped and aligned face images from a video with a neural network. I am considering a use of 3D convolutions in order to capture the spatiotemporal dependency. However,...
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1 vote
0 answers
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Good example of a walk-through of the FCI algorithm to ensure all steps are done

The FCI algorithm is a common algorithm used for learning a Markov equivalence class of causal graphs from observational data. I am wondering if there are any good examples that walk through a causal ...
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7 votes
3 answers
248 views

Test the hypothesis that the performances of k machine learning models on the same test set is the same

I have $k$ Machine Learning models, trained on the same training set, and I want to test the hypothesis that their performance on a fresh test set is the same. See EDIT 1 below for what I mean with ...
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