Cross Validated Weekly Newsletter - Monday, November 30, 2015

Top new questions this week: Have the reports of the death of the t-test been greatly exaggerated? Reading through CV all-time classics I came across a statement that I would like to clarify. This is the post and my question refers to the closing remarks: "I have to note that all of the knowledge I ... t-test wilcoxon paired-comparisons   asked by Antoni Parellada 17 votes answered by Glen_b 17 votes Antonym of variance Is there a word that means the 'inverse of variance'? That is, if $X$ has high variance, then $X$ has low $\dots$? Not interested in a near antonym (like 'agreement' or 'similarity') but specifically ... variance terminology precision   asked by Hugh 12 votes answered by Tim 24 votes Why Are Measures of Dispersion Less Intuitive Than Centrality? There seems to be something in our human understanding that creates difficulties in grasping intuitively the idea of variance. In a narrow sense the answer is immediate: squaring throws us off from ... variance dispersion   asked by Antoni Parellada 9 votes answered by Frank Harrell 6 votes Drawing samples from a multivariate normal distribution subject to quadratic constraints I would like to draw samples $x \in \mathbb{R}^d$ from $\mathcal{N}(\mu, \Sigma)$ subject to the constraint that $||x||^2_2 = 1$ efficiently. distributions normal-distribution sampling multivariate-normal importance-sampling   asked by Sobi 8 votes answered by Xi'an 5 votes When will L1 regularization work better than L2 and vice versa? Note: I know that L1 has feature selection property. I am trying to understand which one to choose when feature selection is completely irrelevant. How to decide which regularization (L1 or L2) to ... regression lasso regularization ridge-regression   asked by ML_Pro 8 votes answered by user777 11 votes Should final (production ready) model be trained on complete data or just on training set? Suppose I trained several models on training set, choose best one using cross validation set and measured performance on test set. So now I have one final best model. Should I retrain it on my all ... machine-learning validation regression-strategies   asked by Yurii 7 votes answered by Frank Harrell 5 votes How to understand the drawbacks of Hierarchical Clustering? Can someone explain the pros and cons of Hierarchical Clustering? Does Hierarchical Clustering have the same drawbacks as K means? What are the advantages of Hierarchical Clustering over K means? ... clustering k-means unsupervised-learning hierarchical-clustering   asked by ML_Pro 7 votes answered by mic 5 votes Greatest hits from previous weeks: Who Are The Bayesians? As one becomes interested in statistics, the dichotomy "Frequentist" vs. "Bayesian" soon becomes commonplace (and who hasn't read Nate Silver's The Signal and the Noise, anyway?). In talks and ... bayesian mathematical-statistics inference bayes frequentist   asked by Antoni Parellada 67 votes answered by Rasmus Bååth 41 votes How to determine which distribution fits my data best? I have a dataset and would like to figure out which distribution fits my data best. I used the fitdistr() function to estimate the necessary parameters to describe the assumed distribution (i.e. ... r distributions goodness-of-fit kolmogorov-smirnov   asked by tobibo 34 votes answered by COOLSerdash 36 votes Can you answer these? Comparing two histograms using Chi-Square distance I want to compare two images of faces. I calculated their LBP-histograms. So now I need to compare these two histograms and get something that will tell how much these histograms are equal (0 - 100%). ... chi-squared histogram image-processing   asked by Anton Golovin 2 votes Deep Learning - What are the senones in a Deep Network? I am reading this paper: skype translator where they use CD-DNN-HMMs (Context dependent Deep neural Networks with Hidden Markov Models). I can understand the idea of the project and the architecture ... machine-learning neural-networks deep-learning hidden-markov-model natural-language   asked by davidivad 4 votes Convergence of expectation Suppose we have $X_n \overset{D}\to X$ for some sequence $X_{1},\dotsc, X_{n}$. Is it the case that if $E(X_{n}^2) \to E(X^2)$ we have it that $E(X_n) \to E(X)$, and when would it hold? My first ... expected-value convergence moments   asked by Asymptote 2 votes

Subscribe to more Stack Exchange newsletters Unsubscribe from this newsletter or change your email preferences by visiting your subscriptions page on stackexchange.com. Questions? Comments? Let us know on our feedback site. If you no longer want to receive mail from Stack Exchange, unsubscribe from all stackexchange.com emails. Stack Exchange, Inc. 110 William St, 28th Floor, NY NY 10038 <3