Research

My research focuses on developing computational techniques for Bayesian spatial modeling. However, I am also is interested in numerical methods (within a deterministic approach) for spatial interpolation purposes.

In Progress

• Cavieres, J., Monnahan, C.C., Bolin., D, 2025. Approximated Gaussian random field: effects of parameterization on MCMC sampling.

Preprints

• Cavieres, J., Krumscheid, S., 2025. regTPS-KLE: A Novel Approach To Approximate A Gaussian Random Field for Bayesian Spatial Modeling. arXiv

• Cavieres, J., Moraga, P., Monnahan, C.C. (2024). Why not a thin plate spline for spatial models? A comparative study using Bayesian inference. arXiv

• Cavieres, J., Karkulik, M. (2022). Efficient estimation for a smoothing thin plate spline in a two-dimensional space. arXiv

Journal Publications

• Cavieres, J., Monnahan, C.C., Bolin, D., and Bergherr, E. (2024). Approximated Gaussian Random Field Under Different Parameterizations for MCMC. Developments in Statistical Modelling

• Escárate, P., Curé, M., Araya, I., Coronel, M., Cedeño, A.L., Celedon, L., Cavieres, J., et al. (2023). A method to deconvolve stellar profiles: The Non-Rotating Line utilizing Gaussian Sum Approximation. Astronomy & Astrophysics

• Lu, M., Cavieres, J., Moraga, P. (2023). A comparison of spatial and non-spatial machine learning methods in NO2 modelling: prediction accuracy, uncertainty quantification, and model interpretation. Geographical Analysis

• Cavieres, J., Ibacache-Pulgar, G., Contreras-Reyes, J.E. (2022). Thin plate spline model under skew-normal random errors: estimation and diagnostic analysis for spatial data. Statistical Computation and Simulation

• Cavieres, J., Monnahan, C.C, Vehtari, A. (2021). Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation. Fisheries Research

• Cavieres, J., Nicolis, O. (2018). Using a spatio-temporal bayesian approach to estimate the relative abundance index of yellow squat lobster (Cervimunida johni) of Chile. Fisheries Research

Conferences & Workshops

• Cavieres, J., Monnahan, C.C., Bolin, D., Bergherr, E., 2024. Approximated Gaussian random field under different parameterizations for MCMC. International Workshop on Statistical Modelling 2024, Durham, England.

• Cavieres, J., Moraga, P., Monnahan, C.C., 2023. Bayesian semiparametric spatial model using Template Model Builder (). CFE-CMStatistics Conference 2023, Berlin, Germany.

• Cavieres, J., Monnahan, C.C., Moraga, P., 2023.A semiparametric thin plate spline spatial model using Bayesian computation. Statistical Computing 2023, Günzburg, Germany.

• Cure, M., Arcos, C., Araya, I., Escarate, P., Celedon, L., Cavieres, J., Pezoa, R., Olivares, E., Farias, G., 2022. Bayesian deconvolution of a rotating spectral line profile to a non-rotating one. XXXI General Assembly of international Astronomical Union, Busan, Republic of Korea.

• Cavieres, J., 2021. Combining all the pieces together to create an efficient full Bayesian geostatistical model: The SPDE method in . 2do Workshop de Estadística: Contribuciones de Posgrado. Sociedad Chilena de Estatística (SOCHE).

• Cavieres, J., Moraga, P., 2021. Fitting spatial random field models using and the SPDE approach: implementation via and a comparative study of two different parametrizations. End-to-end Bayesian learning, Marseille, France.

• Cavieres, J., 2019. Incorporating the spatial dependence with physical barriers in a bayesian spatio-temporal model to obtain a relative index of abundance. StanCon2019, Cambridge, England.

• Plaza, F., Cavieres, J., Salas, R., Nicolis, O., 2018. Deep learning approach for seismic risk assessment in Chile. XIV IEEE Latin American Summer School in Computational Intelligence.

• Cavieres, J., Nicolis, O. 2016. Bayesian spatio-temporal modelling for analyzing the sea urchin (Loxechinus albus) fishery in Chile. COBAL V (Congreso de Estadística Bayesiana de America Latina) , Guanajuato, México.