[HTML][HTML] 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 …
Probability forecasts and their combination: A research perspective
RL Winkler, Y Grushka-Cockayne… - Decision …, 2019 - pubsonline.informs.org
We explore some recent, and not so recent, developments concerning the use of probability
forecasts and their combination in decision making. Despite these advances, challenges still …
forecasts and their combination in decision making. Despite these advances, challenges still …
[图书][B] Spatio-temporal statistics with R
The world is becoming increasingly complex, with larger quantities of data available to be
analyzed. It so happens that much of these" big data" that are available are spatio-temporal …
analyzed. It so happens that much of these" big data" that are available are spatio-temporal …
A permissioned blockchain-based implementation of LMSR prediction markets
A Carvalho - Decision Support Systems, 2020 - Elsevier
Since the seminal work by Hanson (2003), the Logarithmic Market Scoring Rule (LMSR) has
become the de facto market-maker mechanism for prediction markets. We suggest in this …
become the de facto market-maker mechanism for prediction markets. We suggest in this …
Aligning the interests of newsvendors and forecasters through blockchain-based smart contracts and proper scoring rules
A Carvalho, M Karimi - Decision Support Systems, 2021 - Elsevier
We consider a newsvendor setting where the newsvendor elicits a demand forecast from an
expert to determine the optimal inventory level for a product. Since the interests of both …
expert to determine the optimal inventory level for a product. Since the interests of both …
A review of probabilistic forecasting and prediction with machine learning
H Tyralis, G Papacharalampous - arXiv preprint arXiv:2209.08307, 2022 - arxiv.org
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 …
Unraveling hypothetical bias in discrete choice experiments
L Menapace, R Raffaelli - Journal of Economic Behavior & Organization, 2020 - Elsevier
Our study contributes to the literature on hypothetical bias in discrete choice experiments in
two main respects. First, using stated and revealed preference data collected from a sample …
two main respects. First, using stated and revealed preference data collected from a sample …
Evaluation of probabilistic project cost estimates
M Jørgensen, M Welde… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Evaluation of cost estimates should be fair and give incentives for accuracy. These goals, we
argue, are challenged by a lack of precision in what is meant by a cost estimate and the use …
argue, are challenged by a lack of precision in what is meant by a cost estimate and the use …
Group structure and information distribution on the emergence of collective intelligence
More and more decision-making problems are being solved by groups. Collective
intelligence is the ability of groups to perform well when solving complex problems. Thus, it …
intelligence is the ability of groups to perform well when solving complex problems. Thus, it …
Eliciting information from heterogeneous mobile crowdsourced workers without verification
In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to complete
tasks (eg, road traffic sensing) and truthfully report their solutions. When platforms cannot …
tasks (eg, road traffic sensing) and truthfully report their solutions. When platforms cannot …