...
Prefactor |
| Index |
|
|---|---|---|---|
true +/- err. est. | MC | true +/- err. est. | MC |
27.00 +/- 1.78 | 28.19 +/- 1.84 | -2.19 +/- 0.05 | -2.22 +/- 0.05 |
Markov Chain Monte Carlo (MCMC)
Here is a constrasting analysis. Using Markov Chain Monte Carlo, we sample the posterior distribution of the fit parameters for a single trial. (NB: Since the prior distributions of the fit parameters are assumed to be flat, the posterior distribution is the likelihood function.) An advantage of using the Markov Chain is that binning over any single parameter in the chain automatically marginalizes over the remaining parameters.
...
- Geminga
Prefactor
Index
true +/- err. est.
MC
true +/- err. est.
MC
23.29 +/- 1.68
24.38 +/- 1.74
-1.66 +/- 0.03
-1.68 +/- 0.03
MCMC results for a single trial
As before, the blue curves are the best-fit parameter values and error estimates obtained from the inverse Hessian represented as Gaussian functions; the red curves are Gaussians fit to the histograms.
Extragalactic Diffuse
...