Simulated 2D correlated data

In summary, the conversation discusses simulating particles on a lattice with a correlated property, such as color, at a known correlation length. The individual seeking advice is looking for a way to simulate this in higher dimensions, preferably using continuous-valued variables. A possible solution is suggested, involving simulating independent random variables and then defining the correlated variables as linear combinations of them.
  • #1
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Not sure this is the right area to post this.

Let's say I have particles on a lattice, and they all have some property (ie, color) that is correlated at some known correlation length. I want to simulate this! In 1D I could do something like have color be a random walk in the given dimension, but that doesn't work for more dimensions. I'd rather have a continuous-valued variable than not, so spins aren't ideal.

Help?
 
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  • #2
Goldberry said:
they all have some property (ie, color) that is correlated at some known correlation length.
Someone familiar with the physics might known the definition of "correlated at some known correlation length", but if you want advice from the general population of math people, you should define what that means.

As a generality, simulation of correlated random variables can be done by first simulating a set of independent random variables ##X_1, X_2,...X_n## and then defining the correlated random variables as linear combinations of the ##X_i##. I don't know whether doing an analogous thing with stochastic processes would satisfy your requirements.
 

1. What is "Simulated 2D correlated data"?

Simulated 2D correlated data refers to a dataset that has been artificially created to mimic real-world data with known correlations between variables. This type of data is commonly used in statistical and scientific studies to test hypotheses and validate models.

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Simulated 2D correlated data is created using mathematical algorithms or computer simulations, whereas real data is collected from observations or experiments. This allows researchers to control the correlations between variables in simulated data, which is not always possible in real data.

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One advantage of using simulated 2D correlated data is that it allows researchers to test their hypotheses and validate their models in a controlled environment. This type of data also eliminates potential biases and confounding factors that may be present in real data.

4. How is "Simulated 2D correlated data" used in scientific research?

Simulated 2D correlated data is commonly used in statistical and scientific studies to evaluate the performance of statistical methods, such as regression or machine learning algorithms. It can also be used to generate data for simulation studies or to compare different models in a controlled setting.

5. Are there any limitations to using "Simulated 2D correlated data"?

One limitation of using simulated 2D correlated data is that it may not accurately reflect the complexities and nuances of real-world data. This can lead to discrepancies between the findings from simulated data and real data. Additionally, the correlations between variables in simulated data may not accurately represent those in real data, which can affect the generalizability of the results.

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