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Questions tagged [numerical-integration]

A class of algorithms to approximate definite integrals.

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Subtraction of Monte Carlo integrals - Catastrophic cancellation

I am attempting to estimate a quantity $Q$ which is given by the difference between two functions of Monte Carlo integrals over some set of points $\{x_i\}_{i=1}^N$, call the estimator $\hat{Q}$: $$ \...
Eweler's user avatar
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How to compute the expected value of a function of a random variable given its log-density function? [closed]

Given a log-density function $\mathcal{L}f_{X}(x)$ of an 1d continuous random variable $X \in \mathcal{L}^{\infty}$ and an 1d polynomial function $h: \mathcal{I}(X) \to \mathbb{R}$, the expected value ...
Alice Springs's user avatar
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Can Fisher Information Matrix be calculated numerically through finite differentiation?

In GLMs (generalized linear models), the negative of the Fisher information matrix takes the form of a cross product between covariates $\mathbf{X}$ and a diagonal matrix: $$ \mathbf{X}^T \mathbf{D} \...
anymous.asker's user avatar
2 votes
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$E(XY)$ for a truncated bivariate normal

If $(X, Y)$ follows a bivariate Gaussian distribution with mean ${\bf \mu}$ and covariance ${\bf \Sigma}$ with truncation bounds $(a_x, b_x, a_y, b_y)$, can we compute $E(XY)$ in closed form? If not, ...
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Why use integrals when building exponential composite functions?

I recently read this paper, which describes a generalisation for statistical exponential decay models used in ecology. Essentially, the parameter $k$ of the exponential decay function $f(x) = ce^{-kx}$...
Luka Seamus Wright's user avatar
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(Bayesian) Quadrature when the density can be factorised with respect to a directed graph

I am very new to (Bayesian) quadrature and am trying to understand if or how we can use additional information to approximate our definite integral. Specifically, I am interested in the expected value....
Astrid's user avatar
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Numerical moments of a multivariate Poisson Log-normal posterior

I have a log-density of the form: $$P(\mathbf{x}) \propto \exp\left( - \mathbf{b}^{\top} e^{ \mathbf{x} } - \frac{1}{2}\mathbf{x}^{\top}A\mathbf{x} \right)$$ where $A$ is a symmetric positive definite ...
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Numerical quadrature for Pareto distribution

I would like to numerically evaluate an integral of the following type, when evaluating $f(x)$ at any given point is numerically costly: $$ \int_{x_m}^\infty x^{-\alpha}f(x) \, dx, \quad \alpha >1, ...
spellard's user avatar
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63 views

The meaning of probability density functions' product followed by an integration

Scipy's KDE object allows integration of a function multiplied by another KDE object. I assume that this is meant to be used for the estimation of distance between two distributions. As far as I ...
Gideon Kogan's user avatar
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Numerical Optimization of Marginal Likelihood that Explodes

I have a model with a marginal likelihood of the following form: $$\mathcal{L}(\theta_1, \theta_2, \theta_3|\{x_{i,j}\}_{i=1, j=1}^{N, M_i})=\prod_{i=1}^{N}\int_{0}^{1} f(p_i;\theta_1) \prod_{j=1}^{...
Drunk Deriving's user avatar
1 vote
1 answer
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Algorithm to determine sufficient range for numerical integration of PDF to get quantiles

Given a probability density function (PDF), we want to use numerical integration to find a quantile at $\alpha \in (0,1)$. Are there standard algorithms for determining a sufficient and optimal range ...
feetwet's user avatar
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2 votes
1 answer
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Numerical Approximation of Integrals in GLMM

Consider a GLMM setting. Let $i=1,...,m$ denote cluster. Let $l_i(y_i|x_i,b_i;\beta)$ denote the log-likelihood (viewed as a function of the parameters) for each cluster $i$, which includes notably ...
Winston's user avatar
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1 answer
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Calculation of Posterior distribution numerically

For calculating posterior probabilities numerically, I did not understand that why is in the following codes they have divided by 0.001 in the denominator to calculate ...
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Integrating splines over a set of cells

Let $f(s) = \sum_{i=1}^p \beta_i \phi_i(s)$ such that $f: \mathbb{R}^2 \mapsto \mathbb{R}_+$ and the $\phi_i$ are splines (B-splines or Thin-plate splines) and suppose we are interested in $W\subset ...
BelwarDissengulp's user avatar
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Maximum simulated likelihood estimation in R - binary logit example

Very often the likelihood function involves an integral which does not have a closed-form solution (e.g., random coefficients, latent variables). In these cases, we can employ monte-carlo simulation ...
Daniel Heimgartner's user avatar
1 vote
1 answer
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Best way or rules of thumbs for evaluating 1d integrals from randomly sampled grid points

I have a 1D domain (let's say the interval $(0,1)$) on which I randomly sample $N$ points from the uniform distribution. I have a function $f\colon (0,1) \to \mathbb{R}$ which is integrable. What is ...
math_guy's user avatar
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2 votes
1 answer
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MLE for parametric binomial model

I have a model in which $p_i=f(\theta,Z_i)$, where $Z_i$ are iid latent variables distributed with CDF $F_\theta$, and $d_i\sim B(n_i,p_i)$, where $B$ is the binomial distribution. The likelihood ...
user2520938's user avatar
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How to numerically get expectation of a non-linear function of a normally distributed random variable

I'm trying to calculate the expectation of the following numerically: $$\mathbb{E}[V(\theta)]$$ where $\theta\sim N(\mu,\sigma^2)$ and $V(\theta)$ is strictly increasing. I'm struggling to understand ...
Anonymouslylost's user avatar
1 vote
1 answer
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How to use control variate method to estimate $\theta = \int_{1}^{\infty}\frac{x^2}{\sqrt{2\pi}}e^{-x^2/2}dx$

This is the target I want to integrate with Monte Carlo with control variate method: $$\theta = \int_{1}^{\infty}\frac{x^2}{\sqrt{2\pi}}e^{-x^2/2}dx$$ I have checked with Wolfram that it is 0.400626, ...
Ken Hsu's user avatar
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4 votes
1 answer
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Unbiased estimator of an integral raised to a power

I'd like to estimate an integral of the following form using Monte Carlo method: $$ \int_{t_1}^{t_2} g(t) \left[ \int_{- \infty}^{\infty} f(t, u) du \right] ^\gamma dt$$ In case of $\gamma$ being a ...
Dmitri Urbanowicz's user avatar
3 votes
3 answers
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How to use self-normalized importance sampling method to estimate $\int_{1}^{\infty} \frac{x^2}{\sqrt{2\pi}}e^{\frac{-x^2}{2}}dx$?

I want to use self-normalized importance sampling methods to estimate $$\int_{1}^{\infty} \frac{x^2}{\sqrt{2\pi}}e^{\frac{-x^2}{2}} \,dx$$ I choose exponential distribution with rate $\lambda=1$ as ...
Justin 's user avatar
2 votes
1 answer
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Numerical integration over a Random Forest Regression Model

I am trying to compare the accuracy of a polynomial model and a Random forest regression model in predicting a variable Y but also the integral of this variable , With the polynomial model, it is ...
Issamyax's user avatar
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1 answer
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Estimating the integral $\int_{0}^\infty x^4 e^{-2x}\,dx$

We have been given a random variable having a Gamma distribution as shown below: Using the accept-reject algorithm, we are supposed to sample from the Gamma distribution using exponential as the ...
Aswath Gopinath's user avatar
5 votes
1 answer
309 views

How to Find PDF of Transformed Random Variables Numerically?

Is there a way to compute and plot the transformed random variable in Python or R? Let's say if $X_1$ and $X_2$ are independent and identically distributed random variable with PDF defined over non-...
Dhruva Ahuja's user avatar
2 votes
1 answer
96 views

Are discrete mixtures Gauss quadrature-like integral approximations?

I noticed that the formula for Gauss (or Newton-Cotes) quadrature looks very similar to the formula for the PDF of a general mixture distribution. Let $p_{comp}(x)$ be the PDF of a compound ...
ForceBru's user avatar
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1 vote
3 answers
217 views

Why not just run a Markov chain to get stationary probabilities?

I'm reading Performance Modeling and Design of Computer Systems which contains some analysis of Markov chains. In particular, it emphasises various analytical methods for finding the stationary ...
kqr's user avatar
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Estimating the cumulative probability of a bivariate normal distribution

I have a quick question regarding working out the probabilities of a bivariate normal distribution. To my knowledge, there is no nice closed-form for a cumulative distribution function for the ...
Aleksey's user avatar
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2 votes
1 answer
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Bayesian Quadrature to find expectation of unkown function w.r.t. known pdf

I am interested in estimating the integral $\int f(x) P(x) dx$, where $f(x)$ is an expensive function and $P(x)$ is has an analytic form. I would like to evaluate this with as few evaluations of $f(x)$...
kilojoules's user avatar
4 votes
1 answer
129 views

Approximate Posterior Predictive Quantiles with Numerical Methods

I have a posterior function which is easy to approximate using numerical methods (the posterior has only 2 parameters, and is approximately Gaussian because of the large sample). However, I need to ...
Closed Limelike Curves's user avatar
1 vote
0 answers
100 views

Conditional expectation of a normal variable given a lower bound [duplicate]

I saw the identity below but I'm not sure how to derive it. $$E[X\mid X>K] = \mu + \sigma \frac{\phi(z)}{\Phi(-z)} \text{ where } z = \frac{K-\mu} \sigma$$ I'm stuck at the following step: $$E[X\...
submartingale's user avatar
3 votes
1 answer
382 views

Gompertz-Makeham Model Life Expectancy using Numerical Integration and Analytical Solution of Castellares et al. (2020)

I am trying to calculate life expectancies for the Gompertz-Makeham model, but can't replicate the results of the paper which gives the formulas. I use R to ...
Martin Georg Haas's user avatar
4 votes
2 answers
661 views

How to calculate Quasi-Monte Carlo integration error when sampling with Sobol's sequence?

My understanding is that QMC integration using random sampling will converge with $O(\frac{1}{\sqrt{n}})$, while using Sobol's sampling will converge with $O(\frac{(\log{n})^d}{n})$. However I'm ...
Scott's user avatar
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12 votes
5 answers
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Estimate the Euler–Mascheroni constant ($\gamma$) by Monte Carlo simulations

The Euler–Mascheroni constant is defined simply as the limiting difference between harmonic series and the natural logarithm. $$\gamma =\lim_{n\to \infty}\left(\sum _{k=1}^{n}{\frac {1}{k}}-\ln n\...
Bhoris Dhanjal's user avatar
1 vote
0 answers
25 views

What is the expectation of a random variable satisfying some conditions?

How to find the expectation E[X.I(Y<x,X<x)], where X and Y are independent random variables with respective cumulative distribution functions F(.) and G(.) respectively. x is a positive value. ...
reeba mary's user avatar
12 votes
2 answers
3k views

Plain English explanation of Ito's integral?

I'm looking for a plain English explanation of Ito's integral. I don't need an exhaustive proof, derivation, etc. Just a simple ~this is effectively what it does and why it's better than a Riemann sum ...
jbuddy_13's user avatar
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3 votes
1 answer
99 views

Numerically integrate gaussian pdf

Suppose I want to numerically integrate the function $g(\mathbf{x}) = \exp\bigg(-\frac12 \mathbf{x}^\mathsf{T}\mathbf{\Lambda} \mathbf{x}\,\bigg)$ to obtain the normalization constant $$\int_{\mathcal{...
NoVariation's user avatar
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3 votes
1 answer
951 views

Kullback–Leibler divergence between multivariate t and the multivariate normal?

I want to calculate the Kullback–Leibler divergence between a multivariate $t$ distribution and a multivariate normal distribution, for different values of the degrees of freedom $\nu$. However, this ...
Pullback's user avatar
2 votes
0 answers
138 views

Numerical integration of the exact distribution of the product of two random gaussian variables

I am trying to implement this function in R, which is equation (2) in this paper by Cui et al. (2016), which is the exact distribution of the product of two random gaussian variables. ...
POC's user avatar
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1 vote
0 answers
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How do mixed models bypass the overflow inside the integration?

In a mixel model, when you try to maximize the likelihood, you have to integrate over the random effects. This means that you cannot take logs, since what would normally be a product of individual ...
Maeii's user avatar
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1 vote
1 answer
99 views

Computing the probability $P(\exists X\in\{X_1,\ldots, X_N\}:X>\text{max}\{Y_1,\ldots, Y_M\})$

I am trying to somewhat generalize a question, which has been asked in one way or another a several times here on StackExchange**. However, I have not managed to find an answer to the below problem. ...
quantB's user avatar
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2 votes
1 answer
898 views

How to calculate confidence intervals for a generic (non-normal) pdf

In particular, this is a question about distributions with some skew and/or where the mean, median and mode might all be different. For any pdf, it's possible to find multiple different domains within ...
gazza89's user avatar
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0 votes
1 answer
1k views

Different estimates for mixed effects Logistic regression and pwrssUpdate Error message with binomial glmer

Hi this is my first post here. I reported an issue on Github and got valuable help from Dr. Bolker. However, I did some other analyses on the same data and got confused about some of the results. I ...
Tiantian Yang's user avatar
0 votes
0 answers
53 views

Discrete correlation function (sample cross-covariance)

The continuous correlation function for the random variable $A(t)$ at a instant of time $t$ is given by \begin{equation} C_{AA}(\tau) =\frac{1}{T} \int_{0}^T d\bar{t} A(\bar{t})A(\bar{t}+\tau) \end{...
sined's user avatar
  • 101
-3 votes
1 answer
107 views

Positive or negatively bounded CDFs [closed]

If $X\in\mathbb{R}^n$ is a continuous random variable whose cumulative distribution function is ordinarily $$F_X(x) = \int_{-\infty}^{\infty} f_X(x) dx $$ what is the meaning of $$F_X(x) = \int_{0}^{\...
develarist's user avatar
  • 4,015
7 votes
1 answer
326 views

Expressing a marginal probability using copulas

Please correct me if I am wrong and kindly provide me with the correct notations. I have two questions: We know that for the variables $(X,Y,Z)\in \mathbb{R}^3$, the marginal joint density $f(x,y)$ ...
Carl's user avatar
  • 1,226
1 vote
0 answers
46 views

How to calculate Integral error from MCMC

I've recently started using Markov Chain Monte Carlo to calculate integrals, but I can't seem to find how to calculate the error for such an integral. In the standard case of importance sampling, the ...
AlphaBetaGamma96's user avatar
1 vote
1 answer
494 views

Computational Complexity of Multivariate Normal CDF

I'm going to post this here also as per user suggestions since I feel like the root cause of the issue is more maths related than code related. I'm working on a multivariate cross-entropy minimization ...
Tommaso Belluzzo's user avatar
1 vote
1 answer
158 views

Finding confidence interval for unimodal function equivalent to and comparable with standard deviation of normal

I'm trying to characterise an arbitrary, unimodal distribution in a way that is a) easily understandable (to a physics audience) and b) comparable with a normal distribution. My thinking goes like ...
darudiith's user avatar
3 votes
1 answer
266 views

Numerical integration with measurement errors: what scheme to use?

I have an unknown 'smooth' function that is sampled at some $n$ equally spaced locations $x_i$ between $0$ and $1$, with some measurement values $f(x_i)=y_i$, but each has some Gaussian uncertainty ($(...
Ewoud's user avatar
  • 151
1 vote
1 answer
360 views

Calculating variance of MC estimator of an integral

Let $$ f(x) = \sqrt{1-x^2}, \quad I = \int \limits_0^1 f(x) dx. $$ We consider the following estimator of I: $$ \hat{I}_n = \frac{1}{n} \sum \limits_{i=1}^n f(X_i), $$ where $X_1, X_2, ...$ are iid $...
Elizabeth_Banks's user avatar