A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
Bayesian additive regression trees: A review and look forward
Bayesian additive regression trees (BART) provides a flexible approach to fitting a variety of
regression models while avoiding strong parametric assumptions. The sum-of-trees model is …
regression models while avoiding strong parametric assumptions. The sum-of-trees model is …
[BOOK][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion)
This paper presents a novel nonlinear regression model for estimating heterogeneous
treatment effects, geared specifically towards situations with small effect sizes …
treatment effects, geared specifically towards situations with small effect sizes …
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
G Papacharalampous, H Tyralis - Frontiers in Water, 2022 - frontiersin.org
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …
fields, including hydrology. Several machine learning concepts and methods are notably …
A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
Tail forecasting with multivariate Bayesian additive regression trees
We develop multivariate time‐series models using Bayesian additive regression trees that
posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged …
posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged …
Ensuring the robustness and reliability of data-driven knowledge discovery models in production and manufacturing
The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted
framework in production and manufacturing. This data-driven knowledge discovery …
framework in production and manufacturing. This data-driven knowledge discovery …
Predictive distribution modeling using transformation forests
Regression models for supervised learning problems with a continuous response are
commonly understood as models for the conditional mean of the response given predictors …
commonly understood as models for the conditional mean of the response given predictors …
On theory for BART
Ensemble learning is a statistical paradigm built on the premise that many weak learners
can perform exceptionally well when deployed collectively. The BART method of Chipman et …
can perform exceptionally well when deployed collectively. The BART method of Chipman et …