logpost: Log posterior density function
In sgolchi/BPD: Bayesian Particle Discovery

Log posterior density function

1 2	logpost(lambda, mass, mu_s, theta, tau, beta, y = y, G = G, temp, cnew, snew, p0 = 0.5, mu_var, back_mean = "B polynomial")

`lambda`	vector of background values, the same size of grid `G`
`mass`	mass value
`mu_s`	signal streangth parameter
`theta`	correlation parameter of the background logGP prior
`tau`	variance parameter of the background logGP prior
`beta`	polynomial coefficients of the background mean of the background logGP prior
`y`	vector of observations
`G`	grid over mass range
`temp`	temperature parameter
`cnew`	matrix of signal normalizing constants - dimensions are (size of grid `G`)by(number of categories/channels,41)
`snew`	matrix of signal widths, same dimensions as `cnew`
`p0`	prior probability of $mass(Higgs)=0$
`mu_var`	prior variance of signal strength parameter
`back_mean`	parametric form used to specify the mean of background logGP prior