Professor Johnson's research interests focus on Markov chain Monte Carlo (MCMC)
algorithms. Her goal is to develop a set of practical yet theoritically sound conditions
under which it is possible for practitioners to determine the amount of simulation effort
required to achieve prespecified levels of accuracy in the MCMC procedure. 

Johnson is currently a Ph.D. candidate in the School of Statistics at the University of
Minnesota. 

In addition to her academic work, Johnson worked with the National Center for Infectious
Diseases, Centers for Disease Control and Prevention in Fort Collins. 

EDUCATION: B.A., University of Wisconsin, 2001; M.A., Colorado State University, 2003 

Johnson has been teaching at Macalester College since 2009.

Articles

PDF

Gibbs sampling for a Bayesian hierarchical general linear model (with Galin L. Jones), Electronic Journal of Statistics (2010)
 

Presentations

Convergence rates of variable-at-a-time algorithms, The Thirteenth Meeting of New Researchers in Statistics and Probability (2010)
 

How long is long enough?, Department Seminar, Department of Statistics, University of Toronto (2010)
 

Thesis

Geometric ergodicity of Gibbs samplers (University of Minnesota) (2009)