code: def compute_landmarks(
      domain,
      stats,
      sigma,
      prevalence_pval=0.5,
      prevalence_threshold=0,
      threshold=3.0,
      smin=5,
      method="prior",
      algorithm="density",
      n_iter=1000,
      burnin=100,
  ):
      """Compute the  Bayesian Structural Activation patterns
  
      Parameters
      ----------
      domain: StructuredDomain instance,
              Description of the spatial context of the data
      stats: array of shape (nbnodes, subjects):
             the multi-subject statistical maps
      sigma: float > 0:
             expected cluster std in the common space in units of coord
      prevalence_pval: float in the [0,1] interval, optional
                       posterior significance threshold
      prevalence_threshold: float, optional,
                            reference threshold for the prevalence value
      threshold: float, optional,
                 first level threshold
      smin: int, optional,
            minimal size of the regions to validate them
      method: {'gauss_mixture', 'emp_null', 'gam_gauss', 'prior'}, optional,
             'gauss_mixture' A Gaussian Mixture Model is used
             'emp_null' a null mode is fitted to test
             'gam_gauss' a Gamma-Gaussian mixture is used
             'prior' a hard-coded function is used
      algorithm: string, one of ['density', 'co-occurrence'], optional
                 method used to compute the landmarks
      niter: int, optional,
             number of iterations of the DPMM
      burnin: int, optional,
              number of iterations of the DPMM
  
      Returns
      -------
      landmarks: Instance of sbf.LandmarkRegions or None,
                 Describes the ROIs found in inter-subject inference
                 None if nothing can be defined
      hrois: list of  nipy.labs.spatial_models.hroi.Nroi instances
             representing individual ROIs
      """
      hrois, prior_h0, subjects, coords = _compute_individual_regions(
          domain, stats, threshold, smin, method
      )
  
      landmarks, hrois = _bsa_dpmm(
          hrois,
          prior_h0,
          subjects,
          coords,
          sigma,
          prevalence_pval,
          prevalence_threshold,
          algorithm=algorithm,
          n_iter=n_iter,
          burnin=burnin,
      )
  
      return landmarks, hrois
  
file_path: nipy/nipy/labs/spatial_models/bayesian_structural_analysis.py
imports: import numpy
  import scipy.stats
  from nipy.algorithms.graph import wgraph_from_coo_matrix
  from ...algorithms.statistics.empirical_pvalue import NormalEmpiricalNull, gamma_gaussian_fit, three_classes_GMM_fit
  from .hroi import HROI_as_discrete_domain_blobs
  from .structural_bfls import build_landmarks
label_desc: Use image node_id 6 for calling a global function with example usage: compute_landmarks(domain, stats, sigma, prevalence_pval, prevalence_threshold, threshold, smin, method, algorithm, n_iter, burnin) and returns: landmarks,  hrois