Trending Research
On Bayesian Generalized Additive Models
In this paper, we discuss GAMs from the Bayesian perspective, focusing on linear additive models, where the final model can be formulated as a linear-Gaussian system.
Methodology
Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter
This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions.
Applications Systems and Control Systems and Control
ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members.
Applications Computation
(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data
Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully capture the uncertainty in our predictions.
Computation Methodology
Semiparametric Discovery and Estimation of Interaction in Mixed Exposures using Stochastic Interventions
This study introduces a nonparametric definition of interaction and provides an approach to both interaction discovery and efficient estimation of this parameter.
Methodology
Asymptotic analysis of the jittering kernel density estimator
We give an in-depth analysis of the jittering kernel density estimator, which reveals several appealing properties.
Methodology
BigVAR: Tools for Modeling Sparse High-Dimensional Multivariate Time Series
The R package BigVAR allows for the simultaneous estimation of high-dimensional time series by applying structured penalties to the conventional vector autoregression (VAR) and vector autoregression with exogenous variables (VARX) frameworks.
Computation
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales
For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum.
Methodology
Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms
Liesel is a new probabilistic programming framework developed with the aim of supporting research on Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations in general and semi-parametric regression specifications in particular.
Computation
A Comparison of Likelihood-Free Methods With and Without Summary Statistics
We also conduct a substantive empirical comparison with summary statistic based likelihood-free methods.
Methodology Computation
