Questions tagged [multivariate-distribution]
Probability distribution over vectors (as opposed to univariate distributions that are over numbers).
4
votes
2answers
58 views
Derive multivariate from bivariate normal distribution
Could anyone help me on the following.
Let $K$ and $M$ be integers so that $K\geq3$ and $2\leq M < K$. Let $\boldsymbol{X}=(X_1, ..., X_K)^T$ be a random vector, $\boldsymbol{\mu}$ be a $K\times 1$...
0
votes
0answers
32 views
How Do I interpret this Q-Q plot?
I am new to statistics and I am having trouble interpreting the Q-Q plot above. Given the number of outliers in the plot, can I assume that the dataset being tested has a lot of bad data? Should I ...
0
votes
1answer
47 views
When does Bayesian classifier act as linear classifier?
I am reviewing my lectures in Machine Learning and my current topic is Bayesian Classifier. The context is the classification of two classes C1 and C2.
My book (neural networks and learning machines ...
0
votes
0answers
8 views
How to calculate multiple correlation coefficients and test hypothesis?
I found the estimation of S and $\overline{x}$ of a data set to be approximately
$\overline{x} = \begin{pmatrix}
185 & 151 & 183 & 149 \\
\end{pmatrix}'.$
and
$S = \begin{pmatrix}
95 &...
0
votes
1answer
17 views
Correlation between two multivariate measures
I'm reading a paper, but I'm with a problem. The authors say:
Let $\boldsymbol{X} = (X_1, \ldots, X_p)^T$ be a vector $m \times 1$ whose the estimative of variance is proportional to $\boldsymbol{\hat{...
0
votes
0answers
13 views
Frank copula with no dependence
From Wikipedia, the Frank copula is the function $C(u, v)$ such that:
$$C(u, v) = \frac{1}{\theta} \log\!\left[ 1+\frac{(\exp(-\theta u)-1)(\exp(-\theta v)-1)}{\exp(-\theta)-1} \right]$$
for $\theta\...
1
vote
0answers
17 views
Sufficient conditions for multivariate MGF to be finite in neighborhood of zero
In the one-dimensional case, we have the following fact: (see Existence of the moment generating function and variance for proof)
Proposition: The mgf $m(t)$ is finite in an open interval $(t_n,t_p)$ ...
1
vote
0answers
23 views
Estimating Pearson correlation for multivariate t distribution using Kendall rank correlation
I had a task to generate a certain amount of samples for $(X_{1},X_{2})$ with a bivariate $t$ distribution $t_{2}(\nu,\mu,\Sigma)$ and then estimate Pearson's correlation coefficient $\rho$ by using ...
0
votes
0answers
16 views
Projection of multivariate distribution to lower dimensional subspace
Say that $X \in \mathbb{R}^n$ is a vector of $n$ r.v.'s with pdf $p(x_1,\ldots,x_n)$. Let's consider now the linear map $Y = A X$ where $Y \in \mathbb{R}^m$ with $m < n$. I am seeking $p(y_1,\ldots,...
1
vote
1answer
59 views
Multivariate conditional entropy
I would like to take data columns and compute the multivariate conditional entropy. For instance, suppose I have columns $A, B, C D, E$ and I want to compute the conditional entropy $H(E | A,B,C,D)$. ...
0
votes
1answer
48 views
Multivariate distribution: calculate P(Y > b/2)
The joint probability function looks like this:
The first step for calculating $P(Y > 2/b)$ is calculating $f_Y(y)$.
Which I did like this:
The problem here is that my x is still in my indicator,...
0
votes
1answer
96 views
Understanding this expression of the multivariate t-distribution
I found this SO post which expresses the PDF of a multivariate t-distribution in terms of the gamma and normal distribution in python as follows
$$
G = \Gamma (k = \nu /2 ; \theta = 2 / \nu)\\
Z = N (...
1
vote
0answers
38 views
Multivariate $t$ ('mvt') - adjustment in emmeans in R
I am doing post-hoc comparisons of contrasts based on linear mixed models I built in R. I am using the emmeans package for the comparisons. One of the default ...
0
votes
0answers
98 views
multivariate normal distribution with mean vector 0 and covariance matrix Σ
I am newby in statistics and I have huge data with "p" variables and "n" samples. My data is a two dimensional matrix with "n" columns (each column is a sample) and "p" rows (each row is a variable). ...
0
votes
1answer
38 views
Unbiased estimator for Theta of a Normal Distribution
If $X_1,\ldots,X_n\sim \operatorname{iid} \operatorname N(\theta, \sigma^2)$, then verify that $\bar{X}_n$ is unbiased estimator for $\theta$ and that Cramer Rao bound is met?
I am facing difficulty ...
0
votes
1answer
53 views
Finding joint probability distributions from marginal distributions
Question:
I was solving test papers where I found this one.
My doubt:
I know to work with conditional probabilities and Jaccobian Transformation and part A and B can be done applying the above..But ...
0
votes
1answer
47 views
Mean and variance of probability density with multidimensional indicator function
I encountered the following question while studying machine learning:
We are asked to calculate mean and covariance of a given probability density function
$$p(x) = \frac{1}{16} \cdot 1_{0 \leq x_1 ...
1
vote
0answers
25 views
How to compare two multivariate datasets
I have two datasets consisting of 20 features, one set contains 50k records and the other 70k. I want to check if they are from the same population.
Datasets contain discrete features and the ...
4
votes
2answers
52 views
How to compare a new measurement to an existing multivariate distribution?
I have a dataset that describes the position and rotation of an object at different points in time using four dimensions. I want to use this sample of observations to get a sense of what positions and ...
0
votes
0answers
86 views
Deriving predictive distribution
In Bayesian Regression, I am confused how to to get $f*$ and $\sigma*$, given
$$y^∗ \mid \vec{y}\sim\mathcal{N}(f^∗ , σ^∗ )$$
$$
p(y^* \mid \vec{y}) = \int{p(y^* \mid \vec{w}) p(\vec{w} \mid \vec{y})...
3
votes
0answers
46 views
Equality of two multivariate normal CDF's
Let $\pmb{X} \sim N_d(\pmb{\mu}, \pmb{\Sigma})$ and $\pmb{Y} \sim N_d(\pmb{\nu}, \pmb{\Omega})$; $\pmb{\mu} \neq \pmb{\nu}, \pmb{\mu} \neq \pmb{0}, \pmb{\nu} \neq \pmb{0}$, and $\pmb{\Sigma}\neq\pmb{\...
5
votes
1answer
70 views
multivariate Student's t distribution: intuition for non-independence?
Consider a multivariate Student's t distribution, with parameters $\nu$ (d.f.), $\mu$ (location) and $\Sigma$ (shape).
Does anyone have a good intuition for the individual components not being ...
0
votes
1answer
79 views
Variance of sum of random vectors - a proof
For nonrandom matrices $A(rXk)$,$ B(rXm)$, and $c(rX1)$, how does one show that
$$\newcommand{\Var}{{\rm Var}}\newcommand{\Cov}{{\rm Cov}}\newcommand{\*}{{\times}}
\Var(AX+BY+c)=A\Var(X)A′+ B \Var(...
1
vote
0answers
23 views
Multivariate Test for Mean Equivalence
I am looking for an equivalence test for multivariate means (arbitrary or normally distributed).
Any suggestions or hints in the right direction are appreciated.
1
vote
0answers
27 views
Expectation of expressions involving sample covariance matrix and inverse of covariance matrix
Let $y_{ij}$, $i=1,2,\cdots,n_j$ be a random sample from $N_p(\mu_j,\Sigma_j)$, $j=1,2$. Let $$\overline{y}_j=\frac{1}{n_j}\sum_{i=1}^{n_j}y_{ij} \hspace{2mm} \mathrm{and}$$
$$S_j=\frac{1}{n_j-1}\sum_{...
2
votes
0answers
40 views
Check whether a random sample comes from an elliptical distribution?
How can I check whether it is a reasonable assumption to say that a multivariate sample $x_1,...,x_n$ comes from an elliptical distribution, such as a normal distribution or a t-distribution?
In the ...
0
votes
0answers
30 views
How to use Copulas to Combine Multivariate Conditional Probability with Univariate Conditional Probability?
This is sure to be an odd one, but here goes.
I'm trying to estimate P(X|Y, Z) by the distributions of P(X|Y) and P(X|Z). I've thus far been trying to using copulas to achieve that aim, but I'm not ...
4
votes
1answer
64 views
A Multivariate Distribution for Linear Combinations of Independent Exponential Random Variables
Consider a random vector $\mathbf{X} \in \mathbb{R}^r$ whose components $X_j$ are independent exponential variables with different scale parameters $\beta_j$, $j=1,\dots,r$. Suppose I have a general $...
1
vote
0answers
46 views
Mutual information between multivariate random variables? [duplicate]
I have read that mutual information only works with two random variables,
and that for 3 or more RVs there seems to be a variety of different measures (synergy, partial information decomposition, and ...
1
vote
1answer
77 views
Conditional distribution of multivariate Rayleigh distribution
The correlated Rayleigh envelopes using a set of zero-mean complex Gaussian RVs (Random Variables) is given by
$$G_{k}=\sigma_{k}(\sqrt{1-\lambda_k^2}X_k+\lambda_kX_0)+i\sigma_{k}(\sqrt{1-\lambda_k^2}...
7
votes
0answers
102 views
Is multivariate Cauchy stable?
I am trying to prove (if possible), for given $A_{n\times n}$ and $B_{n\times n}$, there exists a $C_{n\times n}$ satisfying $$A\pmb{X}_1 + B\pmb{X}_2 \stackrel{D}{=} C\pmb{X},$$ where $X_1, ~X_2$, ...
1
vote
3answers
96 views
Hessian of Log of Matrix-t distribution
I am trying to calculate the hessian of the log of the matrix-t distribution. I know that the log of the matrix-t distribution can be written:
$$\log T_{N\times P}(X| \nu, M, \Sigma, \Omega) \propto -\...
0
votes
0answers
20 views
Bayesian updating of MVN with joint signal
Suppose t and l are independent variables that are normally distributed with known means and variances. Suppose I get a signal s = f(t, l), where I know f(.) which is deterministic. How do I define ...
1
vote
1answer
72 views
If $\mathbf{x} \sim N(\mathbf{0,I})$ and $\mathbf{y} = \mathbf{Ax}$, what does $\mathbf{A}^T \mathbf{A}$ represent?
If $\mathbf{x} \sim N(\mathbf{0,I})$ then $\mathbf{AA}^T$ is the covariance matrix of $\mathbf{y} = \mathbf{Ax}$, but what does $\mathbf{A}^T \mathbf{A}$ represent?
In some places I have seen ...
0
votes
0answers
18 views
Multivariate goodness-of-fit diagnostics
In a multivariate setting, we can assess the goodness-of-fit of a $p$-dimensional multivariate distribution to a set of $p$-dimensional multivariate data. Using, for example, the squared Mahalanobis ...
0
votes
0answers
80 views
Joint distribution of sample means from multivariate normal with unknown covariance matrix
The distribution of a (standardised) sample mean from a univariate normal distribution with unknown variance is given by the Student's t-distribution (Student's t-distribution).
What about a joint ...
2
votes
1answer
209 views
MGF of the multivariate hypergeometric distribution
Does the multivariate hypergeometric distribution, for sampling without replacement from multiple objects, have a known form for the moment generating function?
4
votes
0answers
54 views
Multivariate stable distribution
I know that if $\pmb{X}_1$ and $\pmb{X}_2$ are independent copies of a $n \times 1$ random vector $\pmb{X}$, then $\pmb{X}$ is said to be sum stable in $\mathbb{R}^n$ if $a\pmb{X}_1 + b\pmb{X}_2 \...
0
votes
0answers
65 views
How does one choose a random isotropic direction and then have the vector have norm 1? [duplicate]
I want to choose a random vector in high dimensions such that it all directions have the same uniform chance (i.e. isotropic in all directions). My current idea is the following algorithm:
sample v ...
0
votes
0answers
22 views
Mutual information for independent subsystems with non-Gaussian distributions
I seem to have found contradiction when computing mutual information for a multivariate Cauchy distribution. Below, I list a few things that I think are true. But I expect that at least one of them ...
4
votes
1answer
245 views
Joint distribution of independent t-distributed random variables
The multivariate t distribution seems to be defined as a "ratio" of a vector of normal random variables and a single gamma (or chi-squared) random variable (independent from the vector of normal).
...
1
vote
0answers
200 views
Can we apply a constraint on the distribution of the layer output?
As far I understood, the hidden layer outputs can be anything based on the learning algorithm or optimization rules. I was wondering if it possible to some constraints on the layer output. For ...
1
vote
0answers
92 views
Transforming a multivariate binary time series to be stationary
I have a multivariate (multi-response) dataset with, for example, 10 different binary responses. I'm interested in an AR(p) model, determining how the responses at previous time steps relate to the ...
0
votes
0answers
28 views
Categorical probability distribution that captures “some” permutation invariance / mirror symmetry
I'm fitting something similar to a naive Bayes model to a data set where each data point has six features, $A_1$, $B_1$, $C_1$, $A_2$, $B_2$ and $C_2$. $A_1$ and $A_2$ can both take values in {$a_{1}$,...
2
votes
1answer
203 views
Correlation matrix for multivariate Cauchy distribution
I have found an equation for the entropy of a $p$-variate Cauchy distribution here [page 70]:
$H(X,R) = \frac{1}{2}\log(\det(R))+f(p)\,,$
where $X=(X_1,X_2,\dots,X_p)$ is vector of random variables ...
1
vote
1answer
107 views
Why do I need multivariate normality tests?
I am new to time series analysis and would like to test a multivariate time series (12 components) for normality. I found several straightforward normality tests and some multivariate normality tests. ...
0
votes
1answer
94 views
finding Pr(X+Y > 500) given the following joint probability density function
I am reviewing for a probability and statistics class. I am stuck on a problem despite repeated attempts. (THIS IS NOT HOMEWORK!)
The questions is:
Consider an electronic system with two components. ...
0
votes
1answer
56 views
Show that $(\mathbf{x}, \boldsymbol\Theta\mathbf{x}+\boldsymbol\eta)$ is jointly normal
This is from Theodoridis' Machine Learning, exercise 3.16.
Suppose $\mathbf{x}$ is a vector of jointly normal random variables
with covariance matrix $\boldsymbol\Sigma_x$. Let $$\mathbf{y} = \...
2
votes
0answers
49 views
Why is conditional independence more important than marginal independence?
Graphical models are based on the idea of representing certain types of conditional independences in a (joint) distribution via a graph, and are an active research area.
As argued (correctly I ...
2
votes
3answers
1k views
What's the difference between Multivariate Gaussian and Mixture of Gaussians?
What's the difference between Multivariate Gaussian and Mixture of Gaussians?
If I have a Multivariate Gaussian and making all the data into ONE vector, is that a Mixture of Gaussians in 1 dimension?...
