# Questions tagged [simulation]

A vast area which includes generating results from computer models.

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### Learning a symmetric distribution: best practice for how to treat samples?

There are 169 different types of Texas Hold-em hands. I want to learn the probability of each of them winning through empirical simulation. Note that I'm ignoring all betting considerations (even ...
• 401
1 vote
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### How to sample from a distribution with discrete variables with a known average?

My motivation is to produce carcasses of animals in an ecosystem. The animals have discrete sizes in kg (75, 216, 700, 2500, 5000, 8500, 25000). I also have the estimated percentage each animal ...
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• 123
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### Simulate data based on current dataset

I would like to simulate some data based on a real dataset in order to train some models. The dataset contains student grades which looks like they fit on a log-gamma distribution. In addition, from ...
61 views

### Bayesian calibration of computer simulations - Likelihood function calculation

I am starting to study Bayesian calibrations of computer models. I am not a statistician and just starting to learn so bear with me if I do not use the correct terminology. The general approach is ...
• 121
653 views

### Simulating Survival Times

I am interested in learning about how to simulate survival times from a Survival Model (e.g. Cox-PH). For example, suppose we fit a Cox-PH Model on some data in R: ...
• 7,030
36 views

### Simulating a mixture model in time series

Let $\Phi (\cdot)$ be the cdf of the standard normal distribution. Given $(y_t)_{t \in \mathbb N}$ a time series. Suppose $F(y_t | \mathcal{F}_{t-1})$ is the conditional cumulative distribution ...
• 234
69 views

### Simulating likelihood ratio test (LRT) pvalue using Monte Carlo method

I'm trying to figure out my assignment to simulate lrt test p-value output using the Monte Carlo method. As far as I understand, the lrt test is supposed to test for "better", more accurate ...
45 views

### Why is it easy for the Gibbs sampler to take long time to converge to target distribution?

This is related to Gelman's Bayesian Data Analysis 3rd Edition pg 300 first paragraph of Section 12.4. The book says the following. "An inherent inefficiency in the Gibbs sampler and Metropolis ...
• 1,315
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### Performance of a bootstrap

In the context of a simulation study, for a bootstrap methodology to estimate a parameter: Should a lots of bootstrap (but each one have a few resamples) be favoured over doing few bootstrap (but each ...
18 views

### How can I generate time series with strong autocorrelation?

I'm new in time series analysis. Given an observed time series, actually the history price of certain asset, I tried to resample it to generate surrogate time series for testing purpose. After ...
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100 views

### Evaluate integral using R [closed]

I need to evaluate $\displaystyle{\int_{1}^{1}\int_{1}^{1}\int_{1}^{1}(y)e^{x+}}dxdz}$ using in R. Here is my attempt: ...
1 vote
51 views

### R statistics: compare bayesian bootstrap to frequentist bootstrap for statistics: univariate odds ratio for small sample [closed]

Greetings to the community, I am seeking assistance in finding a solution to the challenges I am facing. OBJECTIVES: I aim to estimate the univariate odds ratio for a binary exposure in a population. ...
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### Monitoring convergence of MCMC convergence

I am reading Gelman's BDA 3rd Edition chapter 11 sec 4 on page 284. I do not see what the book means in the following. "When performing inference for extreme quantiles, or for parameters with ...
• 1,315
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### Correctly visualizing the ergodicity of an AR(1) in R

I'm trying to visualize the mean- ergodicity of a time series and I'm having tremendous doubts about which simulation to use. I show them both and I would like to know which one is correct? Suppose ...
26 views

### How to use Gibbs sampler to simulate normal-normal hierarchical models?

This is related to Gelman's BDA 3rd Edition Chapter 11, Sec 3. The book says the following. "The Gibbs sampler is the simplest of the Markov chain simulation algorithms, and it is our first ...
• 1,315
20 views

### How to simulate multivariate posterior distribution with a flat prior in general?

If I know that the posterior $p(\theta_1,\dots,\theta_m|y)$ can be written $p(\theta_1|\theta_2,\dots,\theta_m,y)p(\theta_2|\theta_3,\dots,\theta_m|y)\dots p(\theta_m|y)$ where $p(\cdot|y)$ in each ...
• 1,315
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### Interpretation of strange result on mediation analysis (with simulated data)?

I simulated some data to test a mediation analysis. If I do an "old style" mediation analysis (following this tutorial), I can clearly see the result that I expected: Total effect ...
• 130
100 views

### Use monte carlo simulation to predict Y variable (linear regression) of a given dataset, and estimate the parameter coefficients

It's my first time learning Monte carlo simulation, I have been given a task to predict the average Y variable (dependent variable) using a given dataset and to estimate the values of the parameter ... 27 views

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