On sequential Monte Carlo, partial rejection control and approximate Bayesian computation (2008)
Peters, G. W., Fan, Y., Sisson, S. A.
We present a sequential Monte Carlo sampler variant of the partial rejection control algorithm introduced by \shortciteNliu01, termed SMC sampler PRC, and show that this variant can be considered...
Francis, Andrew R., Luciani, Fabio, Sisson, S. A.
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with...
Francis, Andrew R., Luciani, Fabio, Sisson, S. A.
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with...
Tanaka, Mark M., Francis, Andrew R., Luciani, Fabio, Sisson, S. A.
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with...
Tanaka, Mark M., Francis, Andrew R., Luciani, Fabio, Sisson, S. A.
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with...
Sequential Monte Carlo without likelihoods
Sisson, S. A., Fan, Y., Tanaka, Mark M.
Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite...