- #1
BRN
- 108
- 10
Hello everybody,
I am working on a Python project in which I have to make Bayesian inference to estimate 4 or more parameters using MCMC.
I also need to evaluate the evidence and I thought to do so through the Laplace approximation in n-dimensions:
$$ E = P(x_0)2\pi^{n/2}|C|^{1/2} $$
Where C is the parameter's covariance matrix and ##P(x_0)## is the maximum value that assumes the posterior.
Getting the covariance matrix is not a problem, but I don't know how get FX0. If they were only 2 parameters I could use matplotlib.hist2d, but being more than 4 parameters...
How could I do?
Some idea?
Thank you!
I am working on a Python project in which I have to make Bayesian inference to estimate 4 or more parameters using MCMC.
I also need to evaluate the evidence and I thought to do so through the Laplace approximation in n-dimensions:
$$ E = P(x_0)2\pi^{n/2}|C|^{1/2} $$
Where C is the parameter's covariance matrix and ##P(x_0)## is the maximum value that assumes the posterior.
Getting the covariance matrix is not a problem, but I don't know how get FX0. If they were only 2 parameters I could use matplotlib.hist2d, but being more than 4 parameters...
How could I do?
Some idea?
Thank you!