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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22 views

PDF of a sum of dependent variables

This is a direct continuation of my recent question. The thing that I actually want to get is the distribution of $a+d+\sqrt{(a-d)^2+4bc}$, where $a,b,c,d$ are uniform in $[0,1]$. Now, the ...
2
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0answers
19 views

Moments and density tails

Assume that the first $n$ moments $m_1,\dots\,m_n$ of a random variable $X\in\mathbb{R}$ are known, but not its probability density function $p(x)$. Does there exist a methodology to characterize ...
10
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2answers
119 views

What's the distribution of $(a-d)^2+4bc$, where $a,b,c,d$ are uniform distributions?

I have four independent uniformly distributed variables $a,b,c,d$, each in $[0,1]$. I want to calculate the distribution of $(a-d)^2+4bc$. I computed the distribution of $u_2=4bc$ to be ...
1
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0answers
10 views

Student t distribution: standardization [duplicate]

I got a question concerning the standardization of a student t distribution. I see that the "plain vanilla" t distribution has density $f(x|\nu)=\frac{\Gamma(\frac{\nu+1}{2}) ...
2
votes
2answers
17 views

Piecewise-constant density estimation

I came across the term "piecewise-constant density estimation" in a paper and haven't been able to find a definition for it online or in my textbook resources. No example was given in the paper ...
0
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0answers
19 views

Integration of (Gaussian pdf)/r. from 0 to R [closed]

Integration of exp(-((r-t)/(sigma*sqrt(v)))^2)/r. as this has no defined anti-derivative. I Have tried to approximate the exp() function but that is working to. suggest some other way around. i dont ...
0
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0answers
24 views

Getting the probability density kernel estimator with R

I am working on a density estimation project and I need to get an estimation of the density as well as an equation for the density estimator (and not the estimate). I am working with kernel ...
1
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2answers
63 views

Probability from normal distribution: < vs <=

I want to calculate the probabilities $P\{X < 0.5\}$ and $P\{X \leq 0.5\}$. $X$ is standard normally distributed. From what I have learned density function $\text{df}(x)$ I can get $P(X = x)$ ...
1
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1answer
19 views

Probability of event depending on a variable with a given distribution

Let $A$ be an event that happens with probability $1-\alpha$, where $\alpha$ has the density function $f_\alpha(x)=3(1-x)^2$ for $0\leq x \leq 1$. Thinking analogous with the discrete case*, I came ...
0
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0answers
21 views

Observation Likelihood in hidden Markov models

As far as I understand, in discrete HMM, the observation symbol probability distribution $b_{i}(O_{t})$ is always a probability less than 1, e.g. $\frac{1}{6}$ for each side when rolling a dice. But ...
1
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1answer
34 views

Marginal Probability Density Function of Joint Distribution

I have this question regarding marginal probability density function of joint distribution. Following is the equation I have. $$f(x,y) = \begin{cases} \frac{3}{2} y^2 & 0 \le x \le 2 \text{ and } ...
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0answers
21 views

Values of PDF in Bayes Classifier

I'm new here and also a beginner in statistics. I'm implementing a Bayes classifier for two classes but get confused with the value of likelihood (pdf). $$P(c|o) = p(o|c)\cdot P(c)/p(o);$$ Here ...
2
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2answers
104 views

In the definition of probability density function, does it matter if the interval is open or closed?

I can find two definitions of Probability Density Functions in the sources I have checked: $$P(a < X < b) = \int^b_a f(x)dx$$ Ref: Hogg & Tanis, Probability and Statistical Inference and ...
0
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0answers
34 views

Likelihood of a Poisson-described event to occur in the next second

Consider a recurring event for which the time periods between consecutive events is exponentially distributed. For argument's sake, I'm waiting for a taxi on a busy street. How might one calculate the ...
2
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1answer
31 views

Choosing among PDFs

This is a pretty broad question. I just learned that two random variables can have the same moments but different PDFs. Take $\mathbb{E}[X_i] = \mu$ and $\mathbb{Var}[X_i] = \sigma^2$. Since there are ...
0
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1answer
29 views

conditional probability, change of variable and Jacobean

I have a question, and I am guessing that the question arises due to my lack of good understanding in the change of variable technique. I would like to evaluate $f_X(x)$. When $f_Y(y)$ exists, I can ...
3
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2answers
79 views

How to measure similarity of bivariate probability distributions?

I have three different distributions of 2D data: or Now I like to know whether distribution two is more similar to distribution one (2 to 1) than distribution three is to distribution one (3 to ...
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0answers
32 views

Accounting for measurement bias using a histogram or violin plot and numerical data in R

The Problem I have a gardener whose job it is to measure trees in meters using a measuring stick. She measures the heights of 1,000 trees. I plot these measurements on a violin plot in R. I am ...
2
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1answer
49 views

How to show that the t-distribution density function is a pdf?

We know that the pdf of the t-distribution is : $$f(t|p)=\frac{\Gamma(\frac{p+1}{2})}{p^{\frac{1}{2}}\Gamma(\frac{1}{2})\Gamma(\frac{p}{2})}\cdot\frac{1}{(1+\frac{t^2}{p})^{\frac{p+1}{2}}} \;\;\;\; ...
0
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0answers
25 views

When to use CRPS (Continuous Rank Probability Score)? What are the alternatives? What are the advantages and disadvantages?

Please correct me if I'm wrong, crps is new for me. I want to understand it better. We have to minimize crps, which is based on the cumulative distribution function of the data. While the information ...
2
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1answer
30 views

Can I use glm with Poisson family if counts data are treated as density?

Imagine you have data of birds counted in an area - let's say, you count 18 birds in a surveyed area of 1,3 km^2. Imagine you relate this counts to 1km^2, so that you have 13.9 parrots per km^2. ...
0
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1answer
11 views

How to compute the pdf for logit/probit models?

According to the probit/logit models, the change in probability due to a change in an explicative variable x is given by the following equation: P(Y = 1 |X) = ...
2
votes
1answer
30 views

About the burr distribution 3 parameters

In my papers the probability density for a burr distribution is given as $f(x) = \dfrac{\gamma \tau \alpha^{\gamma}x^{\tau - 1} }{(\alpha + x^{\tau})^{\gamma + 1}}$ however i have encountered this ...
2
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0answers
56 views

Where is the maximum bias and variance in a histogram as non-parametric density estimator?

I am a little bit confused about bias and variance of non-parametric density estimators and hope you can help me. Assuming a constant bandwidth and sample size, I am wondering at which points of the ...
10
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3answers
241 views

Why does a Cumulative Distribution Function (CDF) uniquely define a distribution?

I have always been told a CDF is unique however a PDF/PMF is not unique, why is that ? Can you give an example where a PDF/PMF is not unique ?
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20 views

95% region from a bivariate density

I have a bivariate data which can be displayed as a scatterplot of which marginal distribution is Uniform(0,1). Using 'kde2d' function in R, I can also obtain two-dimensional density of these data. ...
0
votes
1answer
29 views

Consistency of density estimation under marginalization

Let $(x_1,y_1),\ldots,(x_n,y_n)$ be samples from some unknown distribution $p(X,Y)$ and $\hat{p}(X,Y)$, $\hat{p}(Y)$ density estimates of the joint and marginal distributions (i.e., for the estimation ...
0
votes
3answers
57 views

Intuition of pdf of a continuous random variable [duplicate]

What is the intuition behind the probability density function of a continuous random variable? Integrating it within two points provides the probability that is associated between two points, but if ...
4
votes
1answer
45 views

Linear transformation of a random variable by a tall rectangular matrix

Let's say we have a random vector $\vec{X} \in \mathbb{R}^n$, drawn from a distribution with probability density function $f_\vec{X}(\vec{x})$. If we linearly transform it by a full-rank $n \times n$ ...
2
votes
2answers
73 views

Mean Preserving PDF Spreading

I have a histogram representing the PDF of an unknown discrete RV. The histogram is asymmetrical. To be clear, all I have is the histogram. Is there a known way to increase/decrease the variance of ...
2
votes
1answer
30 views

marginal conditional distribution from MCMC output [duplicate]

I have a MCMC sampler that targets $$\mathbb{P}(U_1,U_2,...U_n \mid G(U) \leq 0)$$ where $U=(U_1,U_2,...U_n)^T$. I realize now I am more interested in estimating the conditional density $$p_k = p(u_k ...
1
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1answer
40 views

Density Function Estimation

Given a sample of $n$ observations, which are assumed to be $i.i.d.$ and generated from a continuous probability law. Consider the question of estimating the density function $f(x)$. There are two ...
3
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1answer
156 views

Show that $\min(U,1-U)$ and that $\max(U,1-U)$ are uniform

Let $U$ be uniform on $(0,\ 1)$. Show that $\min(U,\ 1-U)$ is uniform on $(0,\ 1/2)$ and that $\max(U,\ 1-U)$ is uniform on $(1/2,\ 1)$. I'm not sure how to approach... the only hint i have is that a ...
4
votes
2answers
93 views

Finding the Mean and Variance from PDF

A random variable $n$ can be represented by its PDF $$p(n) = \frac{(\theta - 1) y^{\theta-1} n}{ (n^2 + y^2)^{(\theta+1)/2}}.$$ $\theta$ is a positive integer and $y$ is a positive parameter. If ...
12
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3answers
1k views

“The total area underneath a probability density function is 1” - relative to what?

Conceptually I grasp the meaning of the phrase "the total area underneath a PDF is 1". It should mean that the chances of the outcome being in the total interval of possibilities is 100%. But I ...
2
votes
1answer
59 views

Get probability distribution function from density function

For a given density function, how does one find its distribution function? For example, I have a density function: $f(x)= \begin{cases} t ^2 / 9 & \text{if } t \in (0,3)\\ 0 ...
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1answer
30 views

Interpretation of cartesian product of the support of marginal distribution

Suppose we have a multivariate data set, $s = (s_1, s_2, ... s_p)$ and each $s_i$ is distributed with a distribution that has finite support (we'll call each $s_i$ a "source"). Let us denote the ...
-1
votes
1answer
19 views

On transformations of random variables, discrete vs continuous [duplicate]

Suppose we have a discrete r.v. $X$, take $Y = g(X) $ where $g$ is one-to-one and onto- If we want to obtain the new pdf for the discrete r.v. we simply notice that $$f_Y(y) = P(Y=y) = f_X(g^{-1}(y)) ...
3
votes
3answers
188 views

The probability of a random variable being larger than a sequence of random values

Suppose we have a fixed, known, $n$, and each $x_1 \ldots x_n$ is a random number generated uniformly over $[0,1]$. What is the probability that $x_n$ is the largest value in the sequence?
3
votes
1answer
36 views

PDF of mixture of random variables that are not necessarily independent

I am trying to derive the expression for the PDF of a weighted mixture of n random variables. Let us taken $n=3$ and define $$X = \alpha_1 S_1 + \alpha_2 S_2 + \alpha_3 S_3$$ $$E[X^2] = 1$$ $s_1$, ...
-1
votes
1answer
39 views

question about a Rosenthal inequality

What is the usefulness of Rosenthal inequalities in (kernel) density estimation where $\xi _i .... \xi _n$ are independent random variables, $\mathbb{E}\xi_{i} =0$ and $c(p)=15p/lnp$ for $p>2$ ...
2
votes
1answer
43 views

Compare two unnormalized density functions given the values at samples

I have the density values of two unnormalized density functions $p$ and $q$ at 2000 points: $\mathbf{p} = (p(x_1), p(x_2), \dots p(x_{2000}))$, $\mathbf{q} = (q(x_1), q(x_2), \dots q(x_{2000}))$. Now ...
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0answers
27 views

How to modeling the movement of an object? [closed]

I have implemented the condensation algorithm in order to track a moving object in video sequences, so I would improve the predictive step. Currently the state includes only the coordinates of the ...
0
votes
1answer
44 views

Update Rules in Expectation Maximization

I am emulating a certain PDF behaviour using a function. However, due to divergent improper integral, I don't have a closed form expression for the normalization constant. To get the PDF, I just ...
3
votes
1answer
307 views

Find the mode of a probability distribution function

I am trying the find mode of a probability distribution function given by \begin{equation} g(x/\alpha,\beta,\sigma)=\frac{1}{\Gamma \left( \alpha ...
2
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0answers
37 views

Geometric construction of copula - question regarding C-volume

I am learning about copula's, using Nelsen's book, and more specifically about the geometric method of constructing copula's. The problem is replicated in the following link: ...
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0answers
13 views

How does one analytically solve for the contours of an arbitrary bivariate density?

This slide deck shows how to solve for the contours of a multivariate normal, using an eigendecomposition of the covariance matrix. This seems to rely on the convenient ellipsoid shape of the normal's ...
4
votes
2answers
155 views

Density plot of parameter estimates from linear regression model

I am running a linear regression model in R: data(iris) fit1.iris = lm(Sepal.Length ~ Petal.Length+Petal.Width , data=iris) summary(fit1.iris) These are my ...
1
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1answer
113 views

Mean and variance of ranks

Consider rank data 1 to n with two groups, n=n1+n2, how would one test the null that the two groups have equal rank distributions using MOMENTS? (Wilcoxon is not the answer) Is MLE possible to do the ...
3
votes
2answers
173 views

Assume $X\sim N(\mu,\sigma^2)$. What is the pdf of $X^3$

I have the following that is remaining unanswered and would love some help: Assume $X\sim N(\mu,\sigma^2)$. What is the pdf of $X^3$. For a large sample, $n$, what is the variance of the cube of the ...