sib.Params(
  prob_i = Uniform(1.0), 
  prob_r = Exponential(0.1), 
  pseed = 0.01, 
  psus = 0.5, 
  softconstraint = 0):
    
    Settings the parameters of the model used for inference.

    Parameters
    ----------
    prob_i : Function - optional
        function to compute the probability of infection, 
        depending on the difference (t-ti), ti time of infection.
        [default: Uniform]
    prob_r : Function - optional
        function to compute the probability of recovery, 
        depending on the difference (t-ti), ti time of infection.
        [default: exponential decay with mu = 0.1]
    pseed : Float - optional
        Prior to be source of infections (infected at starting times).
        [default 1e-2]
    psus: Float - optional
        Prior to be susceptible.
        [default: 0.5]        

  
    Returns
    -------
    None

sib.FactorGraph(
  Params,
  contacts = [],
  tests = [],
  individuals = []
)
    Construct the factor graph from the list of contacts,
    the lists of observations, the Parameters of the model.
    Eventually is possible to set infectiousness and 
    recoverability priors on each individual.

    Parameters
    ----------
    Params: Class sib.Params -- optional
        see sib.Params
    contacts: list<tuple(int, int, int, float)>
        list of single direct contacts (for bidirectional contacts add also
        the inverse contact). Order of tuple contact: (node_i, node_j, time, lambda)
        The lambdas are the instantaneous probability of infection.
    tests: list<tuple(int, sib.Test, int)>
        list of single observation on single node a time t. Order of observation tuple: (node_i, sib.Test, time). sib.Test is a class containing the probability to be susceptible, infected, recovery, given by medical tests or symptoms.
    individuals: list(tuple(int, prior_t, prior_r)) [optional]
        List of tuple where each tuple are the prior on the infectiousness and recoverability of node "i".

    return
    ------
      Class FactorGraph

sib.Test(
  ps = 0.0, 
  pi = 0.0, 
  pr = 0.0
)
Evidence given by a test.
    Parameters
    ----------
    ps: probability to be susceptible
    pi: probability to be infected
    pr: probability to be recovery

sib.iterate(
  f,
  maxit=100,
  tol=1e-3, 
  damping=0.0,
  callback=(lambda t, err, f: print(t, err, flush=True))
)
Iterate the BP messages.
    Parameters
    ----------
    f: FactorGraph class
      The factor graph. See sib.FactorGraph
    maxit: int -- optional
      max number of iteration of update of BP equation
    tol: float -- optional
      If the error of the update of BP equation is less than `tol`the iteration stop.
    damping: float -- optional
      Damping parameter to help the convergence of BP equation.
    callback: function(iter_n, err, f) -- optional
      Function callback, called after each iteration. Take as argument the number of current iteration (iter_n), error (err), and the FactorGraph (f).