__init__

MCMC.__init__(vars, y, ly, stats=[])[source]

To construct an MCMC object, the user must provide a list of variables, prior distributions and likelihood functions, the measurement vector, a measurement likelihood and optionally a set of stats to evaluate at each step.

Parameters:

  • vars – A list of triples (v,l,j) containing a triple of a variable v, a prior likelihood function l so that l(v) yields a value proportional to the logarithm of the prior probability of value of v, and finally a jump function j, so that v_new = j(ws, v_old) yields a new value for the variable v and manipulates the Workspace object ws so that a subsequent call to the yCalc WSM will compute the simulated measurement corresponding to the new value v_new of the variable v.

  • y – The measured vector of brightness temperatures which must be consistent with the ARTS WSV y

  • ly – The measurement likelihood such that ly(y, yf) gives the log of the probability that deviations between y and yf are due to measurement errors.

  • stats – This is a list of statstics such that for each element s s(ws) is a scalar value computed on a given workspace.