def multiply(dists):
        '''
        multiplies a list of Distribution objects
        '''
        if  not all([isinstance(k, Distribution) for k in dists]):
            raise NotImplementedError("Can only multiply Distribution objects")

        n_delta = np.sum([k.is_delta for k in dists])
        min_width = np.max([k.min_width for k in dists])
        if n_delta>1:
            raise ArithmeticError("Cannot multiply more than one delta functions!")
        elif n_delta==1:
            delta_dist_ii = np.where([k.is_delta for k in dists])[0][0]
            delta_dist = dists[delta_dist_ii]
            new_xpos = delta_dist.peak_pos
            new_weight  = np.prod([k.prob(new_xpos) for k in dists if k!=delta_dist_ii]) * delta_dist.weight
            res = Distribution.delta_function(new_xpos, weight = new_weight,min_width=min_width)
        else:
            new_xmin = np.max([k.xmin for k in dists])
            new_xmax = np.min([k.xmax for k in dists])

            x_vals = np.unique(np.concatenate([k.x for k in dists]))
            x_vals = x_vals[(x_vals>new_xmin-TINY_NUMBER)&(x_vals<new_xmax+TINY_NUMBER)]
            y_vals = np.sum([k.__call__(x_vals) for k in dists], axis=0)
            peak = y_vals.min()
            ind = (y_vals-peak)<BIG_NUMBER/1000
            n_points = ind.sum()
            if n_points == 0:
                print ("ERROR in distribution multiplication: Distributions do not overlap")
                x_vals = [0,1]
                y_vals = [BIG_NUMBER,BIG_NUMBER]
                res = Distribution(x_vals, y_vals, is_log=True,
                                   min_width=min_width, kind='linear')
            elif n_points == 1:
                res = Distribution.delta_function(x_vals[0])
            else:
                res = Distribution(x_vals[ind], y_vals[ind], is_log=True,
                                   min_width=min_width, kind='linear', assume_sorted=True)

        return res
multiplies a list of Distribution objects