# Questions tagged [wishart]

The Wishart distribution is a common matrix distribution on square symmetric semi-definite matrices.

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### Covariance Matrix Eigenvalue Distribution Relation to Size [closed]

I'm trying to run PCA on sample covariance matrices of various sizes (ranging between 20 x 20 to 4000 x 4000). Assume the data follows a joint multivariate normal distribution. While derivations are ...
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### Intuitive explanation for Marchenko-Pastur law

I am looking for an intuitive reasoning behind the Marchenko Pastur law, which is described as a law of large numbers analog for random matrices. I know the law gives the probability density function ...
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### Show that distribution of $\small(n-1)\overline{X}'(S^{-1}-\frac{S^{-1}\mu_0\mu_0'S^{-1}}{\mu_0'S^{-1}\mu_0})\overline{X}$ is $\small T^2(p-1,n-1)$

Show that the distribution of $(n-1)\overline{X}'\left(S^{-1}-\dfrac{S^{-1}\mu_0\mu_0'S^{-1}}{\mu_0'S^{-1}\mu_0}\right)\overline{X}$ is $T^2(p-1,n-1)$ where $X_i\sim N_p(\mu,\Sigma)$, where $\Sigma$ ...
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### Downsides of inverse Wishart prior in hierarchical models

I am working with a Bayesian hierarchical model that has a number of parameters for each experimental unit (6 parameters). I really do not know all that much about them a-priori, but it is quite ...
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### How do I use Stan to fit a covariance matrix? [closed]

I'm new to Stan (and bayesian methods in general), so this is likely very simple. I'm trying to model some multivariate normal data. All I want to know is the covariance matrix generating the data, ...
864 views

### Eigenvectors of a Wishart matrix

I have been trying to find a good source (or clarifications) to help me understand this point. I am very new to random matrix theory so any pointers will be appreciated. Here is what I think I have ...
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### Sampling from Wishart distributions with scalar degrees of freedom $(\upsilon>p-1)$

Let $\upsilon$ be the degrees of freedom of a Wishart distribution and $p$ the dimensions of its scale matrix. If the degrees of freedom is a scalar, then its range is: $$\upsilon > p−1$$ and ...
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### Intuition behind random matrices [closed]

I am looking for an intuition for random matrices. Say, Gaussian or Binary squares matrices to begin with. I am considering three possible viewpoints: As a $n$ random points in $\mathbb{R}^n$. As a ...
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### rWishart: should be $dof>p-1$ or $dof \ge p$?

The degrees of freedom $n$ of a Wishart distribution parametrized like in wikipedia (and like most people do) are restricted to: $$n>p-1$$ where $p$ are the dimensions of the data, to ensure ...
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### Normal-inverse-Wishart distribution

The Normal-inverse-Wishart distribution is a conjugate prior for the multivariate normal distribution when the mean and covariance are unknown. I understand that conjugate priors are mathematically ...
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### What are the parameters of a Wishart-Wishart posterior?

When infering the precision matrix $\boldsymbol{\Lambda}$ of a normal distribution used to generate $N$ D-dimensional vectors $\mathbf{x_1},..,\mathbf{x_N}$ \begin{align} \mathbf{x_i} &\sim \...
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### Question on Inverse-Wishart Distribution when reading Peter Hoff's book

I have a couple of questions when reading the chapter 7 The Multivariate Normal Model of Peter Hoff's "A First Course in Bayesian Statistical Methods". First, could anyone give me any resource about "...
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### Distribution of Trace of non-centered Wishart matrix

I am looking for the distribution of trace of the non-central Wishart matrix with different variations along different axes. Is there a general formula for such distribution? If not, is there a ...
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### Bayesian estimation of multivariate Gaussian from noisy observations with known error variances

I have a dataset $\mathbf{D} = \{ (\tau_i, \Gamma_i) : 1 \le i \le n \}$ of observations $\tau_i = X_i + \epsilon_i$ from a $p$-dimensional Gaussian $X_i \sim \mathcal{N}(\mu, \Sigma)$ contaminated by ...
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### Marginal distributions of off-diagonal terms in a Wishart-distributed random variable

I am interested in finding expressions for the marginal distributions of the off-diagonal terms in a Wishart-distributed random variable. More specifically, suppose $X$ is an $n \times p$ matrix, ...
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### Sum of independent Wishart with same degrees of freedom but different scale matrices

Is there any result showing that a sum of independent Wishart with same degrees of freedom but different scale matrices is a Wishart? For example, if I have two random variables:  Y \sim W_p(n,\...
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### Posterior covariance of Normal-Inverse-Wishart not converging properly

I am trying to implement a simple normal-inverse-Wishart conjugate prior distribution for a multivariate normal with unknown mean and covariance in numpy/scipy such that it can take a data vector and ...
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### Sample from Wishart distribution with inverse Scale matrix

I tried to model precision matrix in a hierarchical Bayesian setup with Wishart prior given d.f. and inverse scale matrix, and matrix normal likelihood, since it's a conjugate prior, my posterior on ...
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### Entropy of Inverse-Wishart distribution

What is the entropy of the Inverse-Wishart distribution? I need just a reference, but derivation (e.g. using inverse property) would be interesting too.
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### Matching X'X with Wishart Samples in R

$X'X \sim Wishart(\Sigma,n)$, however I'm having a tough time producing this in R. Example: ...
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### How to specify the Wishart distribution scale matrix

I'm running the below Bayesian mixing model in R using the rjags package, but I am having difficultly in specifying the scale matrix for the Wishart distribution. Essentially, I want Sigma.inv to be a ...
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### How do you translate a density from Cholesky factor to density of the matrix?

Suppose $L$ is a random $p\times p$ lower triangular matrix, with known density, $f(L)$. To compute the density of $C=L L^{\top}$, one needs to use the change of density formula. This is a little bit ...
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### Generate covariance matrix with fixed values in certain cells

I want to be able to generate a covariance matrix of dimensions $D$ x $D$, such that certain specified cells of this matrix contain a fixed predetermined values (at least approximately). For e.g. For ...
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### Degrees of freedom for Gaussian Process

I am reading this paper on Generalised Wishart Process (GWP). It is about modelling covariance matrix of D - dimensional gaussian processes (GP) as GWP. I fail to understand interpretation of "degrees ...