Computing the Fisher Matrix numerically

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SUMMARY

The discussion focuses on computing the Fisher matrix numerically, particularly in complex models like the power spectrum influenced by cosmological parameters. The participant highlights challenges in deriving covariances between varying parameters and creating triangle plots from the Fisher matrix. Eric Linder's notes and the MontePython paper are recommended resources for understanding the Fisher matrix approximation in this context.

PREREQUISITES
  • Understanding of Fisher matrix computation in statistical models
  • Familiarity with cosmological parameters and their influence on models
  • Knowledge of numerical methods for parameter estimation
  • Experience with triangle plots for visualizing parameter covariances
NEXT STEPS
  • Review Eric Linder's notes on Fisher matrices at this link
  • Study the MontePython paper for insights on Fisher matrix approximations at this link
  • Learn about covariance matrix computation in parameter estimation
  • Explore advanced techniques for visualizing parameter spaces using triangle plots
USEFUL FOR

Researchers in cosmology, statisticians working with complex models, and data scientists focusing on parameter estimation techniques will benefit from this discussion.

shahbaznihal
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TL;DR
Computation of Fisher matrix for complicated models
Hi,

I have been studying the Fisher matrix to apply in a project. I understand how to compute a fisher matrix when you have a simple model for example which is linear in the model parameters (in that case the derivatives of the model with respect to the parameters are independent of the parameter). But I am unable to understand how to compute the Fisher matrix when the model is more complicated like the power spectrum which depends on the cosmological parameters in a complicated way. From the exercises that I have done, I understand the Fisher matrix computed around certain model parameters is valid around that set of model parameters but how do you go from varying parameters (and computing the Fisher matrix around each set of parameters) to deducing the covariances between the parameters in parameter space and making the triangle plot.

I have been unable to find the answer to this question all day. Thanks in advance for your help.
 
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Hi,

I leave here the reply by Eric Linder from CosmoCoffee which had been useful for me,

You might find useful the notes at https://supernova.lbl.gov/~evlinder/scires.html, specifically https://supernova.lbl.gov/~evlinder/InfoMatrixNotes.pdf.

I also leave this link to the MontePython paper which is a pretty nice description of using the Fisher matrix approximation.

https://arxiv.org/pdf/1804.07261.pdf
 
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