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HDPStructure(X, dist, ...), simulates the HDPStructure
   posterior conditioned on X, and returns population assignments of
   the individuals in the population at each locus of interest.

      X NxT. Allele matrix for the population. Here, X(i,j) denotes
              the allelic value of individual i at locus j.

      dist Tx1. Vector indicating the distance between alleles, in
              centimorgans/Mb. The value of dist(t), for t>1
              indicates the distance between allele t-1 and allele t.
              The value of dist(1) is unused.

      'PARAMETER', VALUE, ... Additional optional arguments must be
              provided as additional arguments to the call to the
              HDPStructure function in 'PARAMETER', VALUE, ... pairs.
              In each pair, 'PARAMETER' specifies which parameter to
              set, and VALUE specifies the value of that parameter.
              The parameters recognised by HDPStructure are listed as

      initpop NxT. The initialization for the population assignment.
              If you wish to start the MCMC chain at a population
              assignment which is already close to a mode, then that
              population assignment can be provided by the 'initpop'
              parameter. If 'initpop' is not provided, then the
              initialization is found using a linkage based method.

      alp0 scalar. We place a gamma prior on the concentration of the
              top level Dirichlet process in the hierarchy. The
              hyperparameters for that gamma prior are given by
              'alp0' and 'bet0'. The default value of 'alp0' is 10.0.

      bet0 scalar. The default value of 'bet0' is 1.0.

      alp scalar. We also place a gamma prior on the concentrations
              of each of the bottom level Dirichlet processes in the
              hierarchy. The hyperparameters for this gamma prior are
              given by 'alp' and 'bet'. The default value of 'alp' is

      bet scalar. The default value of 'bet' is 5.0.

      a0init Tx1. The base measure of the top level Dirichlet process
              of the hierarchy is parameterised by the locations of
              interest. At each location, the base measure specifies
              allele emission probabilities. We place beta priors on
              these emission probabilities. In particular, at each
              location l, the prior on the emission probabilities is
              given by a bet a random variable with parameters a0(l)
              and b0(l). We place a prior on each pair of variables
              a0(l) and b0(l) as follows. First, we introduce
              variables c and m, and we place a symmetric beta prior
              on m with parameters 0.01, 0.01, and then we set
              a0(l) = c * m, b0(l) = c * (1-m). We fix c = .01. By
              default, the initial values of a0 and b0 are chosen by
              matching the empirical allele distributions of the
              data. In order to override this initialization,
              'a0init' and 'b0init' may be provided. Note that these
              are resampled using MH updates during each MCMC step
              according to the priors specified above.

      b0init Tx1. The parameter 'b0init' is described above. By
              default, 'b0init' is chosen to match the empirical
              allele distribution of the data.

      lowL scalar. We place a log uniform prior on the latent
              recombination rate. The range of this log uniform prior
              is given by ['lowL', 'upL']. The default value of
              'lowL' is 0.0.

      upL scalar. The default value of 'upL' is 50.0.

      iters scalar. The number of MCMC iterations to perform is given
              by 'iters'. In each iteration, MCMC updates are
              performed in turn for all of the latent variables of
              the model. In particular, the latent variables are
              updated in the following order, using the following

                  top level DP --- Gibbs
                  bottom level DPs --- MH
                  theta --- Gibbs
                  alpha0 --- MH
                  alpha --- Gibbs
                  recombination rate --- MH
                  a0 and b0 --- MH

              The default value for 'iters' is 10000.

      burnin scalar. The number of MCMC iterations to discard at the
              beginning of the chain before collecting the state of
              the MCMC chain is given by 'burnin'. The default value
              for 'burnin' is 5000.

      skip scalar. The number of MCMC iterations to thin after the
              burnin is completed is given by 'skip'. In particular,
              only every 'skip'-th iteration will be collected after
              burnin. The default value for 'skip' is 5. The value of
              skip must divide iters-burnin.

      seed scalar. For reproducibility, a seed for the random number
              generator driving the MCMC may be provided. If the seed
              is not provided, or if the seed provided is <=0, then
              the state of matlab's random number generator is not
              modified prior to the commencement of the MCMC.

      cores scalar. Number of cores to use in parallel. Defaults to
              one core. The number of cores should be less than or
              equal to N.

      POPULATION NxTxD. This array contains the population assignment
              of the individuals in the population for each state in
              the MCMC chain that is collected. The number of
              collected states is given by D = (iters-burnin)/skip.
              Therefore, the population for individual i at location
              j specified by the state of the m-th iteration of MCMC
              is given by POPULATION(i, j, m).

      RESULTS struct. Additional collected MCMC variables are given
              in the structure RESULTS. These variables are as



      > % Run HDPStructure on X, a binary array of alleles.
      > POPULATION=HDPStructure(X,dist);
      > % Display the population assignments of the last state in the
      > %     MCMC chain.
      > disp(POPULATION(:,:,end));

Copyright (c) 2015, Maria De Iorio, Lloyd T. Elliott, Stefano Favaro
    and Yee Whye Teh.


Software for Bayesian nonparametric population structure analysis based on hierarchical Dirichlet processes.







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