take account of the presence of nuisance parameters. See Basu (1977) for some general discussion and Spall and Garner (1990) for some discussion relative to the identification of parameters in linear dynamic (i.e., state space representation) models. In Bayesian analysis, a generally applicable approach creates random samples from the joint posterior distribution of all the parameters: see Markov chain Monte Carlo. Given these, the joint distribution of only the parameters of interest can be readily found by marginalizing over the nuisance parameters. However, this approach may not always be computationally efficient if some or all of the nuisance parameters can be eliminated on a theoretical basis. See also Adaptive estimator Profile likelihood References Basu, D. (1977), "On the Elimination of Nuisance Parameters," Journal of the American Statistical Association, vol. 77, pp. 355–366. Bernardo, J. M., Smith, A. F. M. (2000) Bayesian Theory. Wiley. Cox, D.R., Hinkley, D.V. (1974) Theoretical Statistics. Chapman and Hall. Spall, J. C. and Garner, J. P. (1990), “Parameter Identification for State-Space Models with Nuisance Parameters,” IEEE Transactions on Aerospace and Electronic Systems, vol. 26(6), pp. 992–998. Young, G. A., Smith, R. L. (2005) Essentials of Statistical Inference, CUP. Estimation theory