Questions tagged [marginal]

The marginal distribution refers to the probability distribution of a subset of variables contained in a joint distribution.

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What does this assumption mean regarding equal marginal densities?

Suppose that we have a random variable $\epsilon$ with density $q(\epsilon)$ and $w = t(\theta, \epsilon)$, where $t$ is a deterministic function of a constant $\theta$ and random variable $\epsilon$. ...
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Why $E$ and $F$ are conditionally independent given $C$ and $D$?

Below is a Directed Acyclic Graph (Fig.a). From this figure, it is said that: $E$ and $F$ are conditionally independent given $C$ and $D$. I am confused about it. Let's assume the causal ...
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Computing a marginal distribution of a joint involving a delta function

Suppose that we have four continuous random variables $x,y,z,$ and $v$ and we want to compute the following integral: $$\int f(x\mid y)f(z\mid x,y)f(v\mid z,x,y)\,dx$$ There are a few conditions: $...
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Why can “one row of this element” in the MCMC output table represent marginal posterior distribution? [duplicate]

Take the table below as an example, which is in Box 8.1 of this book. This table illustrates the converged MCMC output including the first and last five samples for parameters and derived quantities. ...
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From beta distribution to Dirichlet: Estimation of the concentrantion parameters

Searching at least 3 hours about the connection between beta distribution and dirichlet. My problem is: I have a collection of random variables $X_i \sim Beta(a_i, b_i)$. The parameters $a_i$ and $...
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47 views

Inverse distribution of gaussian mixture

In one of the papers I've encountered, the authors propose a copula function $$ c(u_1, \ldots, u_d; \Theta) = \frac{\psi(y_1, \ldots, y_d; \Theta)}{\prod_{j=1}^{d}\psi_j (y_j)}$$ where $\psi(y_1, \...
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17 views

Find marginal distribution

The random vector $(X,Y)$ is uniformly distributed over $$D=\{(x,y): 0 \leq x \leq 2 , 0 \leq y \leq 2-x\}.$$ Find the marginal distribution of the random variables $X$ and $Y$. For the radom vector $...
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Marginalising over Dependent Random Variables

Suppose I have two RVs, $A$, and $B$. Every place I have looked thus far suggests the following for marginalisation, which for me is fine: $f_A(a) = \int_{-\infty}^{\infty} f_{A,B}(a,b)db $. ...
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Getting marginal means for ANCOVA in R (effect vs emmeans)

I'm a bit new to running GLM models in R, so forgive me is this is a silly question. Context: I'm running an ANCOVA with the goal to control for multiple covariates while understanding the ...
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Logit model: Interpretation of interaction effect with only dummies

I have a large binary logit model (cross-section) with many variables, mostly dummies. From the information on age, I have created 6 dummies: 17-24, 25-34, 35-44, 45-54 (reference group), 55-64 and 65+...
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Constraints on choice of marginal distribution and likelihood

For some time I have been reading into Bishop's Pattern Recognition and Machine Learning. Coming back to some earlier chapters the following got me confused and I am interested where, formally I go ...
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Computing conditional probabilities on multivariate data from covariances

I am struggling to implement some Bayesian algorithm which I hope you may help me with. I am required to compute all probabilities of the form: $$P(Z_i\le z_i\;|\;Z_1=z_1, \dots, Z_{i-1}=z_{i-1}) \;\...
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Phase marginal for a multivariate complex Gaussian density

Suppose $z$ is a random variable taking values in $\mathbb{C}^n$ and admitting the complex Gaussian density $p(z;W) \propto \exp{(-\frac{1}{2}z^*Wz)}$, where $W$ is Hermitian. Let $r$ be the vector of ...
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Distribution of random variable with multinomial sampling distribution and parameters $(n,p)$, where $n\sim$ Poisson with truncation

Suppose you have: $$X\mid N\sim\text{MN}(N,p_1,p_2,\ldots,p_{J})$$ $$N\sim \text{Poisson}(\lambda)$$ What is the marginal distribution of $X$? In this case, the answer is simply this. But... ...
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Marginalizing a high-dimensional multivariate Gaussian distribution

I have an 11-dimensional multivariate Gaussian, with a covariance matrix with non-zero values in every element. My goal is to marginalize this down to 4 dimensions, but I'm having some computational ...
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svm's margin equation derive question

I hava question about the margin equation $$\frac{a}{||w||}$$ where this equation coming from? I think it substract the $w^{T} +b -a - w^{T}x +b$ but not sure how margin equation derived
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If $\frac{X_a}{X_a+Y_a}$ and $\frac{X_b}{X_b+Y_b}$ are correlated, what about $X_a+Y_a$ and $X_b+Y_b$

Suppose I have four normal random variables:$X_a$,$Y_a$,$X_b$,$Y_b$. $X_a$ and $Y_a$ follow bivariate normal, $X_b$ and $Y_b$ also follow bivariate normal. let $Z_a=\frac{X_a}{X_a+Y_a}$ , $Z_b=\...
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Setting boundaries for calculating $P(Y/X>2)$ choosing $dx/dy$ order [duplicate]

Given two independent variables $X$ and $Y$, with marginal pdfs $f_X(x)=2x, 0 \le x \le 1$ and $f_Y(y)=1, 0 \le y \le 1$, calculate $P(\frac{Y}{X} > 2)$. So this can be written as $P(Y>2X)$, ...
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Finding the joint CDF using the joint PDF; why can't I do this?

Find the joint CDF of the independent random variables $X$ and $Y$, where $f_x(x)=x/2, 0\le x \le 2, $ and $f_Y(y)=2y, 0 \le y \le 1$. To do this, we can find the CDF separately for each of the ...
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Computation and interpretation of marginal effects in a GLMM

I am currently working on a GLMM model which uses a Poisson distribution and need to compute and interpret marginal effects from this model. The model outcome consists of a count (COUNT) collected ...
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Numerically/approximately integrating over independent gamma variables

Problem Statement For a problem in biology, I am testing out a joint distribution of the form: $$ X \sim Multinomial(\frac{\theta_1}{\sum \theta_i}, ...,\frac{\theta_n}{\sum{\theta_i}}) \\ \theta_i \...
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49 views

Is the marginal distribution of a maximum entropy distribution also a maximum entropy distribution?

I am considering under what circumstances maximum entropy distributions are closed under marginalization. The main case I am interested is the following setting: a finite set of discrete random ...
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29 views

Marginal derivation from joint pdf

I have a uniform prior f(Θ) ~ U(4,10) and a uniform 'observation' model f(X|Θ) ~ U(θ-1, θ+1). Their joint pdf is f(X,Θ)=1/12 for 4 < θ < 10 and (θ-1)< x <(θ+1)  and 0 otherwise. If I ...
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54 views

Finding the marginal distribution

If $p(y) =N(0,\Gamma) $ and if $p(x|y) =N(\Lambda y, \psi I) $where $I$ denotes the identity matrix. Also, we have the conditions: $\Gamma_{ij} =0$ for $i \neq j$ (diagonal) $\Lambda \Lambda^T = I$ ...
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30 views

Help with PCA Question

The conventional model for probabilistic principal component analysis has a standard normal latent $\vec{y}$ and a loading matrix $\Lambda$: $P(\vec{y}) \sim N(\vec{0}, I)$, $P(\vec{x}|\vec{y}) \sim ...
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Continuous Marginals

What are the conditions under which a continuous multivariate probability distribution in $\mathbb{R}^N$ has continous marginals in all dimensions?
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Marginal Effect

If anyone could help me with this question, I would appreciate. in this model salary = B0 + B 1LSAT + B 2LSAT2 + B3log(libvol) + B4rank + u where LSAT is the average standardized test score for ...
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PCA Marginal Distribution

In PCA, if I have a latent $\vec{y}$ with loading matrix $\Lambda$, then the PCA models using: (1) $P(\vec{y}) \sim N(\vec{0}, I)$, $P(\vec{x}|\vec{y}) \sim N(\Lambda \vec{y}, \psi I)$ (2) $P(\vec{y}...
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35 views

Sufficient conditions for equal marginal medians

Let $X, Y$ be dependent random variables taking values on the same set (either a finite set, an interval or the real line). I'd like to know if there's any condition on $P(Y|X)$ which ensures that $$\...
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Sum of conditional probabilities = Marginal probability? [closed]

Is the following true?: $P(X=j) = \sum_i P(X=j\mid Y=i)$ Thanks!
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Marginal Distribution from Bivariate Distribution Matrix

I am doing some practice problems to prepare for my statistics exam, and I just want to know if my reasoning is correct on one problem, and if not, I want to know how I should reason through this. The ...
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marginal distribution of normal $\mu$ with inverse gamma prior on $\sigma^2$

(From Bayesian Essentials with R by Marin & Robert) Given that $$\mu | \sigma^2 \sim N(\epsilon, \sigma^2 / \lambda_\mu)\,,$$ and $$\sigma^2 \sim IG(\lambda_\sigma /2, \alpha /2)\,,$$ we want ...
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Longitudinal Mediation with Inverse-Probabilty Weighting / Marginal Structural Model

Validater, I am currently trying to investigate how mediation analysis can be applied with longitudinal data. I already considered some Structural Equation Models (SEM), e.g. the 3-Wave example of ...
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Bayesian analysis: comparison of marginal probability distributions

Is it valid to compare mariginal probability distributions from separate Bayesian analyses to infer which scenario is most likely? Specifically, in phylogenetic (evolutionary) analysis, if I ...
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Having difficulty deciding limits of integration for a joint to marginal pdf

A joint pdf, $f_{X,Y}(x,y)=5$, is given with the following intervals: $-1<x<1$ $x^2<y<x^2+{1\over{10}}$ I am trying to find marginal pdf of $f_Y(y)$ but I am stuck. Trying for hours....
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48 views

Calculating odds ratio in Multiple select choice question analysis

I have a multiple response categorical variable (MRCV) and a single response categorical variable (SRCV). Respondents in my survey were presented with 6 choices (6 barriers to access an application) ...
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Resample from data with constraints to the marginal distribution

Motivation This problem comes from the situation where I have a non-random sample of individuals for which $p$ variables are measured. The target is to extract a subset of individuals which would be ...
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71 views

Confusion about range of integration for density function

Consider the joint density function: $$f(x,y) = \begin{cases} 2 & & \text{for } 0 \leq x \leq1 \text{ and } 0 \leq y \leq 1-x, \\[6pt] 0 & & \text{otherwise}. \end{cases}$$ From ...
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Can someone explain what the difference is in the p value of the summary output and emmeans (pairwise, least square means)?

I have a model: m_ramet<-glmer(ramets_net ~ salt * typhagroup + ramets_start + (1|site),data=morphdata, family="poisson"(link="log")) and am ...
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177 views

Where is my mistake in this definition of Bayes Factor?

From "The Bayesian Choice" by Christian P. Robert. The definition of the Bayes factor is given to be the ratio of the posterior probabilities of the null and the alternative hypothesis over the ratio ...
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Marginal Log Likelihood of Count Data to Simplify MLE

My question concerns the calculation of a marginal likelihood given some priors for my underlying exponential mixture distribution. Background My data is the (orderd) set of integers $\{N_\ell\}$. ...
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How to specify uniform distribution with same properties as normal distribution?

What I mean is, is it possible to specify a uniform random variable $U$ with random parameters $a,b$, where $a=-b$, and are generated from some other distribution, such that the marginal pdf of $U(a,b)...
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Marginal Distribution of Exponential Mixture Model

I am currently trying to marginalize over the scale parameter in a mixture distribution of exponential pdfs, but I do not trust my result. Let me show you my steps: Probability Density Function The ...
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187 views

marginal posterior distribution

Given $n$ normally distributed observations $f(X|\sigma_X)=\mathcal{N}(\mu_X, \sigma_X^2)$ and assuming a uniform prior on $\log(\sigma_X)$ and known $\mu_x$, I'm trying to find marginal posterior ...
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Joint densities and conditional independece

Let us assume the joint density $p(x,y,z)$ is factorized as $p(y)p(z|y)p(x|z)$. Hence, $x \perp y|z$. Now, the posterior distribution of z is: $p(z|x,y)=\frac{p(x,y,z)}{p(x,y)}$, where $p(x,y)=\int p(...
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marginalization of joint distributions

I am trying to understand the following sentence, section 2.2, in this paper: "...it is required that the joint mode $p(x,z,a)$ gives back the original $p(x,z)$ under marginalization over $a$, thus $...
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MCMC combined with numerical integration towards more efficient Bayesian inference

I am quite new to Bayesian statistics so the question can be a bit naive. My question is on how to deal with a model with individual coefficients. Simple versions of a task and a model I deal with is ...
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Prove that a multivariate density is valid

My question is quite simple. How to prove that a function $f(x_1,x_2,..., x_n)$ is a valid density? I mean, aside of the fact that must integrate to 1, and it must be positive, do i have to prove ...
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Multivariate distribution with specific (multivariate) marginal distributions

Let suppose I have a 6-variate random variable $\mathbf{x}=(x_1,x_2,x_3,x_4,x_5,x_6)$. What I want is to define a multivariate distribution for $\mathbf{x}$ with some specific multivariate marginals. ...
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About calculation of marginalizing (Bishop's book)

I'd like to ask a simple question, but I don't know how to solve it. Basically, it is from Bishop's book pattern recognition. The following figure is from his book. In chapter 8, to show that a and b ...