Unanswered Questions
51,949 questions with no upvoted or accepted answers
32
votes
1answer
1k views
Link Anomaly Detection in Temporal Network
I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
25
votes
0answers
635 views
Bound for Arithmetic Harmonic mean inequality for matrices?
NOTE: This question has originally been posted in MSE, but it did not generate any interest. It was first posted there, because the question itself is a pure matrix-algebra question.
Nevertheless, ...
25
votes
1answer
571 views
Jaynes' $A_p$ distribution
In Jaynes' book "Probability Theory: The Logic of Science", Jaynes has a chapter (Ch 18) entitled "The $A_p$ distribution and rule of succession" in which he introduces the idea of $A_p$ distributions,...
20
votes
0answers
841 views
How does a Relevance Vector Machine (RVM) work?
Relevance Vector Machines (RVMs) are really interesting models when contrasted with the highly geometrical (and popular) SVMs.
In the light of a question like How does a Support Vector Machine (SVM) ...
20
votes
1answer
615 views
Wavelet-domain gaussian processes: what is the covariance?
I've been reading Maraun et al, "Nonstationary Gaussian processes in wavelet domain: Synthesis, estimation, and significant testing" (2007) which defines a class of non-stationary GPs that can be ...
20
votes
0answers
1k views
How to create a multivariate Brownian Bridge
It is known, that a standard multivariate Brownian bridge $ y(\mathbf u) $ is a centered Gaussian process with covariance function
$$ \mathbb E(y(\mathbf u) y(\mathbf v)) = \prod_{j=1}^d (u_j \wedge ...
18
votes
1answer
431 views
Blind source separation of convex mixture?
Suppose I have $n$ independent sources, $X_1, X_2, ..., X_n$ and I observe $m$ convex mixtures:
\begin{align}
Y_1 &= a_{11}X_1 + a_{12}X_2 + \cdots + a_{1n}X_n\\
...&\\
Y_m &= a_{m1}X_1 + ...
18
votes
1answer
1k views
$ARIMA(p,d,q)+X_t$, Simulation over Forecasting period
I have time series data and I used an $ARIMA(p,d,q)+X_t$ as the model to fit the data. The $X_t$ is an indicator random variable that is either 0 (when I don’t see a rare event) or 1 (when I see the ...
17
votes
2answers
2k views
What problem does oversampling, undersampling, and SMOTE solve?
In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...
17
votes
0answers
848 views
Diagnostic plot for assessing homogeneity of variance-covariance matrices
Is there a handy plot for comparing the variance-covariance matrices of two (or perhaps more) groups? An alternative to looking at lots of marginal plots, especially in the multivariate Normal case?
16
votes
0answers
432 views
Distribution of $\frac{\sum_{i=1}^n X_iY_i}{\sum_{i=1}^n X_i^2}$ where $X_i,Y_i$s are i.i.d Normal variables
Suppose $X_1,\ldots,X_n,Y_1,\ldots,Y_n$ are i.i.d $\mathcal N(0,1)$ random variables.
I am interested in the distribution of $$U=\frac{\sum_{i=1}^n X_iY_i}{\sum_{i=1}^n X_i^2}$$
I define $$Z=\...
16
votes
0answers
574 views
Fitting custom distributions by MLE
My question relates to fitting custom distributions in R but I feel it has enough of a probability element to remain on CV.
I have an interesting set of data which has the following characteristics:
...
16
votes
0answers
568 views
Distribution of inverse Wishart to a power?
In a related question, I had asked about the norm induced by an inverse Wishart matrix. I am interested in generalizing that result somewhat. Let $A\sim\mathcal{W}_p\left(I,n\right)$, a Wishart matrix ...
15
votes
0answers
736 views
Bootstrapping Generalized Least Squares
Scenario:
Consider the use of bootstrapping to estimate the distribution of model parameters fitted per a linear or nonlinear generalized least squares model. In particular, assume there is a ...
14
votes
0answers
3k views
Getting started with bayesian structural models using MCMC
I'm trying to learn bayesian structural time series analysis. For a variety of reasons I need to use Python (mostly pymc3) not R so please do not suggest the ...
