Questions tagged [sampling]

Creating samples from a well-specified population using a probabilistic method and/or producing random numbers from a specified distribution. As this tag is ambiguous, please consider [survey-sampling] for the former and [monte-carlo] or [simulation] for the latter. For questions regarding creating random samples from known distributions, please consider using the [random-generation] tag.

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8 views

how to choose a sample?

I need to review 600 exams with burbles by hand (I have a software to read them all). How many exams do I need to review in order to have 95% of chance of my software is working properly? I think ...
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Problem of unbalanced data

unbalanced data is an issue that can effect the performnce of classification model ,several remides can be done to balance the data two of them are upsampling and downsampling , my questions is : how ...
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probability sampling calculating probabilities

Consider a population with 10 elements , N:{0,1,2,3,4,5,6,7,8,9}. The probability of drawing ...
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Seasonality affects samples on a heterogeneous system?

In a time window of a year, suppose that a social networks has a bimodal normal distribution that peaks at April and October approximately. Being a heterogenous social network, it has different types ...
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Standard deviation in Direct-Sampling

In a computational physics course, I was asked to do direct-sampling for the numerical value of $\pi$ and then I estimated the standard deviation of $\pi$ , ...
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statistic significance of proportions

Let's assume someone buys a bag of smarties, and finds that it includes 22 brown candies 19 red candies 16 yellow candies 15 blue candies 8 green candies How can we use a test of significance to ...
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How is random sampling done in practical terms with a large population (e.g,. people suffering from arthritis in the US)?

my first post here. I'm studying statistics and we are learning about random sampling but I don't understand how what I am learning is applicable to the real world. Are there stat books or articles ...
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Multi-arm RCT Sample Size External and Internal valid

I am trying to figure out the sample size for a multi-arm Randomized Control Trial (non-clinical) that will have internal validity and representative of the population, say; population X Any ...
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Is that possible that I use an arbitrary prior distribution that is not conjugate to the likelihood and do mcmc sampling?

In Bayesian Statistics, we can use conjugate priors to the likelihood functions, then we can get the posterior distributions which are the same distributions as the prior distributions. I wonder ...
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Gains on test data set higher than that on training data set post balancing

I have an imbalanced data set (96-4 split between No and Yes cases). I am trying to build a decision tree model for classification after balancing my data set(tried different thresholds for ...
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Correctly Specifying C=0 Sampling Plan

I looking for a little help with how to go about specifying/requiring the use of a C=0 sampling plan on an engineering drawing. In the past, we have typically been able to refer to a standard, for ...
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Random Walk Metropolis Hastings implementation in R using log scale

Context I looked literally everywhere but I couldn't find a complete implementation of the Random Walk Metropolis-Hastings algorithm using the log scale. By log scale I mean that we are working with ...
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2-dimensional inverse transform sampling [closed]

Let $M\subseteq\mathbb R^3$ be a disjoint union of orientable surfaces. In Monte Carlo ray tracing, we sample a surface point $p'\in M$ by drawing a random direction $\omega_{\rm i}\in S^2$ on the ...
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sample size advice for matching nominal variables

I have two databases of patients and medications. Each database has the same patients. Both have millions of records. My job is to select a sample from each and confirm that the medications match. ...
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Sampling from unknown probability distribution [duplicate]

I'm reading about Monte Carlo methods. Suppose that $X_1,...,X_n$ are i.i.d $p(x_i|\theta)$, where $\theta$ is an unknown parameter of interest. My textbook states: Suppose we could sample some number ...
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Binomial distibution with mean 0

I give the following context. Test hypothesis $H_0:p=p_o$ for Bernoulli distributed random variable $X$, with parameter $p$ and samples $X_i$. For estimate $\hat{p}=\frac{1}{n}\sum_{k=1}^n X_i$, $n\...
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Use of the inversion method in sequential sampling to “invert” a random walk

Let $M\subseteq\mathbb R^3$ be Borel measurable, $\lambda$ be a $\sigma$-finite measure on $\mathcal B(M)$, $k\in\mathbb N$, $I:=\{0,\ldots,k\}$, $q$ be a probability density on $\left(E^I,{\mathcal E}...
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Sampling from conditional multivariate normal distribution (“conditioned on ellipses” / stratified sampling)

I have an issue related to stratified sampling. To explain, I need to start with one-dimensional case: Let $N\sim \mathcal{N}(0,1)$, i.e., a standard normal variable with cdf $F$. Partition $\...
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Sampling small set of indices from a very large set

I have a set containing 100 millions of indices. In each iteration, I choose $k$ sub-samples from this set, and once I select the $k$-samples, I increase or decrease the probability for the selected ...
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Can we sample from the wrapped normal distribution and evaluate the density of the sample simultaneously?

In a computer program (written in C++), given $x\in[0,1)$ and $\sigma>0$, I need to sample $y$ from the wrapped normal distribution $\mathcal W_{x,\:\sigma^2}$ with mean $x$ and variance $\sigma^2$ ...
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Likes/Retweets in Twitter data depends on time of extraction

Currently I scheduled a task to extract twitter data of a week (every sunday) to predict the stock market for the following days. The number of likes of a tweet is not static and changes over time. ...
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How biased is a statistical study in which sampling was purposely made without repeats? [duplicate]

[Cross posted on Math.SE] It is understood in mathematical statistics that a sample (as in sampling distribution) may very well contain repeatedly the same item/subject. In practice though, it would ...
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How biased is a statistical study in which sampling was purposely made without repeats?

It is understood in mathematical statistics that a sample (as in sampling distribution) may very well contain repeatedly the same item/subject. In practice though, it would never occur to someone ...
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log sampling distribution?

I am using a python parameter optimization library https://keras-team.github.io/keras-tuner/documentation/hyperparameters/ And here were have the option to define a sampling distribution for various ...
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Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid ...
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Non-stochastic vs Stochastic regressors and sampling distributions and causation?

I was wondering if I understand these correctly. Would an example of a stochastic regressor be weather? so when thinking about the sampling distribtuion and causality, I would think of repeated ...
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Add samples to given set to match distribution

I have a distribution from which I can draw random (iid) samples. I also have a (non-random) "sample" from that distribution that is given and skews heavily towards the lower end of it. I would now ...
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Cochran's sample size formula p variable meaning and intuition

I want to calculate a sample size for a large population of about 50 million. I came across Cochran's formula and the finite population correction. In short, Cochran's formula is the following: $$ n_\...
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Weighted Block Bootstrap

In this diagram (from 'An introduction to the Bootstrap') the black circles represent the original time series while the white circles represent the sampled bootstrap realization. This bootstrap ...
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Can we use Gelman-Rubin diagnostic to assess convergence of parallel tempered chains in MCMC?

I know that the principle behind the Gelman-Rubin diagnostic is comparing within-chain and between-chain variances and if the potential scale reduction factor is less than, say 1.1 or 1.05 then the ...
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How to identify the most difficult cases for classification model of a given type?

I want to build a binary classification model (GLM or GBM) for a small dataset (~100 cases). I would like to find cases that generally have the highest prediction error for the selected model type ...
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Is there any proof of global convergence for 1D convex numeric optimization using cross entropy method?

Suppose we have the following 1D numeric optimization problem: $min_{x} f(x)$ given $0< x \le x_{MAX}$ where $f(x)$ is a convex function. And I want to apply the cross-entropy method to optimize ...
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Inverse transform sampling and ambiguous Intervals

Let $F_i:\mathbb R\to[0,1]$ be a distribution function$^1$ and $$F_i^{-1}(t):=\inf\left\{x\in\mathbb R:F_i(x)\ge t\right\}\;\;\;\text{for }t\in[0,1].$$ I've got a computer program where only $F_i^{-...
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validation of survival analysis model

I am currently looking for evaluating/validating a survival analysis model on quite highly right censored data set. The thing is that i have many individuals in the data set. I wanted to use c-index ...
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type of sampling required

I have to write a commentary on an article about R-E-A-D dogs and their effect on reading ability in children. The article says that for this study 19 children entering the second grade were ...
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Why the nodes in a Boltzmann machine need to be sampled one at a time?

Typically, we use Gibbs sampling to update (or generate samples from) energy based models. This means we update each node while keeping its markov blanket constant. Why can't we update/sample all ...
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How to conceptually think about repeated sampling with instrumental variables?

Say I am using rainfall during a protest as an instrument for political participation on some outcome y. When thinking conceptually about the sampling distribution, what does this mean here? Is it ...
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24 views

How to Test Sampling Methods

Suppose I have a population, $P$, of 100 individuals and I am devising 3 algorithmic methods to sample 20 people from said population: $A$, $B$, and $C$. We know that $C$ is a random, unbiased ...
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I have two sampling techniques $\varphi_1,\varphi_2$. Given $x=\varphi_1(u)$ can I compute a $v$ with $x=\varphi_2(v)$?

I have two sampling surjective techniques $\varphi_1,\varphi_2:[0,1)\to E$ mapping a random number $u\in[0,1)$ to a sample in a measurable space $(E,\mathcal E)$. Say $u\in[0,1)$ and $x:=\varphi_1(u)$...
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kl divergence of two different distributions of subsets

Suppose there is a set $S=\{1, 2, 3, ..., n\}$, then I need a distribution of its subsets with fixed size k, which can be denoted as $A=\{x_1, x_2, ..., x_k\}$ where $x_1$ to $x_k$ are from 1 to n. ...
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Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
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Why is it difficult to sample from Energy Based Models?

I am trying to understand the following claim which is made in the Deep learning book by Goodfellow et. al about a toy energy-based model (with the apparent motivation of introducing Markov Chain ...
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More efficient estimator in this situation?

I am thinking about the following situation (it is an example): There are N families in a city and we have a simple random sample of size n. In that sample we know that there are $x_{0}$ families with ...
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What does MCMC do during burn-in period?

I am studying mcmc and I am wondering what mcmc does during burn-in period. And also what is the difference during burning period and after the burn-in period?
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What is the role of simulated annealing in Gibbs sampling?

While I was reading about Gibbs sampling, I happened to see "simulated annealing" but what is it doing in Gibbs sampling? Although I don't understand the full context of simulated annealing, I am ...
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Does MCMC Gibbs sampling algorithm first build a steady Markov Chain, then does the sampling to build the posterior distribution?

I am currently studying MCMC Gibbs sampling and while reading this part, a question has come into my head if MCMC Gibbs sampling first build a steady Markov Chain and does the sampling or does ...
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Complex survey design

I am currently analyzing a dataset resulting from a complex survey design. Individuals have been selected from a three-stage cluster sampling design with two strates (that have been combined to result ...
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Count distinct from a bootstrapped sample

I have a dataset of N items, with attribute V which can be repetitive, for example: ...
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Shouldn’t we say independent given the distribution?

In statistics we often deal with iid random variables: independent identically distributed. But if we don’t know the distribution (say we still know the support is {0, 1}), and we get a sample x1, say ...
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Are the target population and sample population the same?

pollsters want to know what percentage of all registered voters in Columbus, Ohio, intend to vote in the next election. They visit 400 people from the downtown shopping mall one afternoon and ask ...