Bayesian Nonparametrics & Stochastic Processes Initiative

Welcome to the webpage of the BNP & Stochastic Processes initiative. Seeded by a CONTEX project (2018-9B), the purpose of this forum is to promote and foster research activities around the intersection of two rich areas of mathematical research, Bayesian Nonparametrics and Stochastic Processes.

From its origins Bayesian Nonparametric methods have relied on the theory of Stochastic Processes to create nonparametric priors for statistical analysis. On its counterpart, the theory and frontier applications of Bayesian Nonparametrics has served as a gateway to create new and general stochastic models, reshaping the inference paradigm. The intersection of these two areas results in a fascinating world full of novel and realistic models, able to capture the data complexity of modern data science.

Research Assistants


Qiaohui Lin


Claudia Juaréz


PhD Students


María F. Gil Leyva


Ilia Naumkin


Pablo J. Hernández

MSc Student


Luis E. Reyes

Group Papers

Papers Papers Papers

1 Ayala, D., Jofré, L., Gutiérrez, L. and Mena, R.H. (2020). On a Bayesian nonparametric mixture representation of phase-type distributions. Submitted manuscript.

2 Chae, M. and Walker, S.G. (2020). Wasserstein upper bounds of the total variation for smooth densities. Statistics and Probability Letters. 163: 108771.

3 Gil-Leyva, M.F., Mena, R.H. and Nicoleris, T. (2020). Beta-Binomial stick-breaking nonparametric prior. Electronic Journal of Statistics 14: 1479–1507.

4 Gutiérrez-Peña, E. and Walker, S.G. (2020). On Determining Niche Overlap. Submitted manuscript.

5 Leisen, F., Mena, R.H., Palma, F. and Rossini, L. (2019). On a flexible construction of a negative binomial model. Statistics and Probability Letters 152: 1-8

6 Mena, R.H., Velasco-Hernandez, J.X, Mantilla-Beniers, N.B., Carranco-Sapiéns, G.A., Benet, L., Boyer, D. and Pérez-Castillo, I. (2020). Using the posterior predictive distribution to analyse epidemic models: COVID-19 in Mexico City. Physical Biology, In press. https://doi.org/10.1088/1478-3975/abb115

7 Palma, F. and Mena, R.H. (2020). Continuous-time Markov processes, orthogonal polynomials and Lancaster probabilities. ESAIM: Probability and Statistics 24: 100-112

8 Riva-Palacio, A., Mena, R.H. and Walker, S.G. (2020). On the estimation of partially observed continuous-time Markov chains. Submitted manuscript.

9 Rodríguez, C.E. and Walker, S.G. (2020). Copula Particle Filters. Submitted manuscript.

Manuscripts in preparation


1 Mena R.H. and Walker S.G. Bayesian nonparametric modulated Markov processes. In preparation.

2 Naumkin, I., Mena R.H. and Walker S.G. Mean functionals of Bayesian nonparametric mod- els derived from exchangeability. In preparation.