PDF stands for Probability Density Function. The PDF of a variable gives the relative probability for each value of a continuous variable. Use this tag when asking about probability functions in general, whether PDFs or discrete probability mass functions.

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1answer
28 views

How to test if some data points is drawn from a distribution with linear PDF?

I have some data in the range [0, 1], and from the histogram below, it seems that they might be drawn from a distribution with linear probability density function (what's the name of that kinds of ...
1
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0answers
14 views

Comparing densities of a feature for different classes when the feature is irrelevant to one class

Let us suppose that I have a number of features. I design pdfs for every feature and every class, some of them by smoothing some histogram of training samples, others just by introducing the prior ...
1
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1answer
43 views

Gaussian Mixture Model parameters from density

How do I estimate parameters of subpopulations in a 1D gaussian mixture model when I already have density (measured on a grid) of the mixture? All the algorithms I can find (like the well-known EM ...
5
votes
1answer
72 views

How to statistically compare groups for multiple density plots?

Is there a statistical method to compare these density plots other than ANOVA (MANOVA)? I would like to compare the densities among year within each plot and report which of those distributions are ...
1
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1answer
51 views

Explanation of density rewriting?

Can somebody please explain the math behind this statement to me? I am not sure how they represent the left hand side by that integral and finally how it is proportional to that. \begin{align} ...
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2answers
32 views

How to find pdf of a joint distribution in R?

$F(x,y) =\frac{1}{6}(x^2\, y+x\, y^2)\,,\quad 0\leq x\leq 2,\, 0\leq y\leq 1$ Above is the joint distribution given, how to find out cumulative distribution function of y? how to obtain joint ...
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2answers
44 views

What does drawing sample using Metropolis-Hastings algorithm mean?

I am confused with the word "draw samples from any probability distribution P(x)", mean I apologize for my ignorance, but, drawing sample as i understand, is for example, tossing a coin and writing ...
3
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1answer
32 views

From joint cdf to joint pdf

We can get the joint pdf by differentiating the joint cdf, $\Pr(X\le x, Y\le y)$ with respect to x and y. However, sometimes it's easier to find $\Pr(X\ge x, Y\ge y)$. Notice that taking the ...
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1answer
49 views

How do I compute the density of this data set that is made up of two different 3D-distributions?

A sequel to this question. I have a dataset where: $\frac{4}{5}$ of the points are drawn from: $(x, y) \sim \mathcal{U}_{2}(0,30)$, $(z) \sim \mathcal{U}_{1}(14.5, 15.5)$. $\frac{1}{5}$ of the ...
0
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1answer
20 views

Numerical approximation of percentiles from arbitrary pdf

Given an easily-computable probability density function $f(x)$, what algorithm can we use to numerically approximate percentiles? For instance, we might be looking for $x$ such that given $X \sim ...
1
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1answer
12 views

Area under a truncated distribution = 1

I have computed a truncated normal distribution, which total probability density (i.e. area under the curve) is equal to 0.92. The distribution represents well the reality of the phenomenon I am ...
0
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1answer
44 views

Logarithmic probabilty distribtion and respective problems in matlab

Dear crossvalidated community, quite frankly, I'm not that much into statistics and therefore, I'm having much trouble solving some issues im having in Matlab right now. My plan is to ...
0
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1answer
30 views

Area below probabilities

Let $p$ be probabilities and $D$ is the real How can I proof that the areas $$\int p \; d F_{p}(p|D=1) = \int (1-p) \; d F_{1-p}(1-p|D=1)$$ are equal. Where $F_{p}$ is the empirical distribution ...
3
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0answers
39 views

Formalizing pdf using both discrete and continuous densities

I'm trying to formalize the probability density function for a rather simple process, but I'm having difficulty writing it precisely. Specifically, consider simulating a 1-D Gaussian random walk ...
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0answers
42 views

How can I calculate a discrete Cumulative Distribution MultiDimensional Array from a discrete Probability Mass Array when dimensions > 2?

I would appreciate any help in trying to calculate the Cumulative Distribution Array of a Probability Mass Array when dimensions > 2, essentially a discrete joint cumulative distribution from a sample ...
0
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1answer
32 views

Negative density for non-negative variables [closed]

Having an integer positive variable (number of days) in an experiment, I got negative values for the density plots using R. I have read other posts relating to this topic. They admitted that the ...
0
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1answer
30 views

PDF not matching histogram of synthetic ratios of independent beta

The PDF of the ratios of independent beta variables is described in http://www.tandfonline.com/doi/abs/10.1080/03610920008832632#.U9J02vldUcC To explore the implications, i created an implementation ...
9
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0answers
155 views

Is there a Bayesian approach to density estimation

I am interested to estimate the density of a continuous random variable $X$. One way of doing this that I learnt is the use of Kernel Density Estimation. But now I am interested in a Bayesian ...
4
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2answers
63 views

Non-informative prior for regression model

I'm looking at p. 355 of Gelman's Bayesian Data Analysis (3rd ed.), for which there is no errata, and I see this: In the normal regression model, a convenient non-informative prior distribution ...
2
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1answer
46 views

Discrete analog of CDF: “cumulative mass function”?

We call the integral of a probability density function (PDF) a cumulative distribution function (CDF). But what's the cumulative sum of a probability mass function (PMF) called? I've never heard the ...
11
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3answers
454 views

Do the pdf and the pmf and the cdf contain the same information?

Do the pdf and the pmf and the cdf contain the same information? For me the pdf gives the whole probability to a certain point(basically the area under the probability). The pmf give the probability ...
7
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4answers
455 views

Calculating PDF given CDF

I know that the PDF is the first derivative of the CDF for a continuous random variable, and the difference for a discrete random variable. However, I would like to know why this is, why are there ...
1
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1answer
21 views

Laplace transform and density

It is true that the Laplace transform of a (positive) random variable characterises that random variable, just like its density? ($L_X(z) = E(exp(-Xz))$)
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0answers
15 views

Let $f(x)$ be the density of $X$. What is $\lim_{n\rightarrow \infty}\mathrm{E}_X[nf(X+n)]$?

The expectation can be written as $\mathrm{E}_X[nf(X+n)] = \int_{-\infty}^\infty nf(x+n)f(x)\,\mathrm{d}x$. The expectation relies on the speed of tail $f(x+n)$ goes to $0$. I posted a related ...
2
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0answers
29 views

PDF for ratio of Dirichlet

I have a random vector $\theta$, which was generated by: X1 ~ Dirichlet($\alpha_1$) X2 ~ Dirichlet($\alpha_2$) $\theta$ = ...
6
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1answer
97 views

Sum of normal truncated random variables

Suppose I have $n$ independent normal random variables $$X_1 \sim \mathrm{N}(\mu_1, \sigma_1^2)\\X_2 \sim \mathrm{N}(\mu_2, \sigma_2^2)\\\vdots\\X_n \sim \mathrm{N}(\mu_n, \sigma_n^2)$$ and ...
2
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1answer
25 views

Scale parameter MLE scheme known but how to find according distribution PDF?

For known location, we can find the scale parameter of a normal distribution by calculating the sum of squared differences to the location, then dividing by n-1 and taking the square root. This is the ...
3
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0answers
38 views

How to calculate CDF of g(X)

Let $X$ a random variable with distribution $F_X(x)$ $$Y=g(X) = \left\{ \begin{array}{lr} X-c & : X > c\\ 0 & : -c < X \le c \\ X+c & : X \le -c \end{array} \right\}$$ ...
2
votes
1answer
50 views

Rectifier function of random variable

Let $X$ be a random variable with distribution $F_X$ and density $f_X$. Define $$g(x) = \left\{ \begin{array}{lr} x & : x \ge 0\\ 0 & : x < 0 \end{array} \right\}$$ and let ...
0
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1answer
42 views

Identifying subsets for outlier detection in local outlier factor

I am trying to gain better understanding of the idea of local outliers (as discussed in this pdf) and how the function is implemented. Here are the key passages from the pdf: Local outliers: ...
2
votes
1answer
76 views

Non-central scaled Student's t cumulative density function required (alternatively the pdf)

I need to cite the pdf(density) or cdf(distribution function) of a non-central scaled Student's t distribution. There is an article about the non-central Student's t distribution ...
1
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1answer
65 views

Probability function of three Random Variables multiplied, solidifying intuition

I ran across this exercise: Let $T$ be a random variable distributed as a $\text{Bernoulli}(p)$, $U$ be a random variable distributed as a $\text{Bernoulli}(q)$ and $W$ be a random variable ...
1
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1answer
109 views

Why likelihood is not always a density function? [duplicate]

I try to self-learn Bayesian machine learning (mostly by studying Bishop and Kevin Murphy's books). While working with formulas I was puzzled by the quote that "Note that the likelihood function is ...
0
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0answers
37 views

Moment generating function of a distribution

I want to find the moment generating function (mfg) and mean deviation of this distribution: $$f(x,\epsilon,k,\theta) = k\theta^{(1+1/k+\epsilon/k)}x^{(k+\epsilon)}\exp{(-\theta x^k ...
4
votes
1answer
110 views

Computing inverse probability weights — conditional (multivariate) density estimation?

The general version: I need to estimate $f(A | X)$ where $A$ and $X$ are continuous and multivariate. I'd rather do it nonparametrically because I don't have a good functional form in mind and ...
-1
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1answer
27 views

Normal Probability Density Function and confusion over how it arrives at an answer [duplicate]

I am confused at how the normal distribution's PDF capable of calculating a density for a single variable. I understand that the CDF probability of an exact continuous random variable $X$ is 0. ...
1
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1answer
42 views

How do I compute the density of this distribution

Ferdosi et al define six artificial datasets to test density estimation methods. Part of the fourth dataset is defined as: $$ Uniform(x,y) = [0,100], Gaussian(z) = [M = 50, var = 5] $$ Where $M$ is ...
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0answers
14 views

Goodness of fit of an approximation of a PDF

I'd like to evaluate the performance of an algorithm which learns to approximate a discrete PDF for a latent variable given some noisy input. Now, is there a standard test to evaluate the goodness of ...
5
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3answers
299 views

Let f(x) be some PDF, and F(x) be its CDF. Shouldn't F(x)=.5 give us the expected value of f(x)?

I was playing around in R and have gotten myself very confused about the relation between probability distributions, their expected values, and their cumulative distribution functions. Say we're ...
4
votes
1answer
101 views

Compute pdf of a k-th order statistic

How to compute the density function of the k-th order statistic of a sample of $X_1, X_2, ..., X_n$ random variables distributed independently but not identically (i.e., $X_i \sim F_i$ with $F_i\neq ...
2
votes
1answer
90 views

Difference between two i.i.d Laplace distributions?

What is the PDF of the difference of two i.i.d Laplace distributed random variables? I know that the difference of two i.i.d Normal variables is still the Normal distribution. Since the properties of ...
2
votes
1answer
69 views

What's an intuitive explanation of the F-distribution's PDF? [closed]

I have read on this site an explanation of the t-distribution, and was interested to read a similar one of the F-distribution. The textbooks I have read generally do not cover this topic.
2
votes
2answers
120 views

How to get statistical evidence of similar/different evolution from PDFs

Modified question to better explain the context of my problem: I am studying young stars. When a star is born, it is surrounded by a disk of dust called "protoplanetary disk". Planets form in these ...
3
votes
1answer
45 views

Why do the normal and log-normal density functions differ by a factor?

If a random variable $W$ is Normally distributed, then $\exp(W)$ is Log-Normally distributed. However, the pdfs of these two random variables differ by a factor of $\exp(W)^{-1}$. The Normal pdf ...
0
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0answers
17 views

How do I analytically transform Wishart to inverse-Wishart?

How do I transform the probability density function of the Wishart distribution into that of the inverse-Wishart distribution? I'd like to know the whole process.
4
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2answers
285 views

In R, what does a probability density function compute?

I understand that the probability density function, pdf, of a continuous random variable is the probability of the variable taking on a given value. I am also thought that for a continuous random ...
0
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1answer
42 views

Generating functional-form PDF from Max Likelihood Estimation

For the purpose of this question, please consider me a stats newbie. I'm working on a (very fun!) research project which involves estimating a pdf of "personal values" -- i.e. how much a certain ...
2
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1answer
79 views

Hazard and density function in survival analysis with discrete time

I am running a survival analysis with descrete time. For that purpose I use the R package survival with this function ...
2
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0answers
81 views

kozachenko-leonenko entropy estimation

I'm trying to implement the entropy estimation based on the closest neighbour from Kozachenko and Leonenko but I'm facing a problem I can't solve. The idea is to work in a new set ...
4
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2answers
195 views

How to calculate overlap between empirical probability densities?

I'm looking for a method to calculate the area of overlap between two kernel density estimates in R, as a measure of similarity between two samples. To clarify, in the following example, I would need ...