Coherent combination of probabilistic outputs for group decision making: an algebraic approach

Leonelli, Manuele; Riccomagno, Eva; Smith, Jim Q.

doi:10.1007/s00291-020-00588-8

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Therefore, complexity is intrinsic to massive amounts of data, where high dimensionality and dynamism are often present [3,4]. Nonetheless, all of the information contained in the acquired data can be extracted by using an estimation method, e.g., with maximum likelihood estimation (MLE); a parametric version of such a method will be supported by a supposed distribution.…”

Section: Introductionmentioning

confidence: 99%

Generalizing Normality: Different Estimation Methods for Skewed Information

et al. 2021

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Normality is the most commonly used mathematical supposition in data modeling. Nonetheless, even based on the law of large numbers (LLN), normality is a strong presumption, given that the presence of asymmetry and multi-modality in real-world problems is expected. Thus, a flexible modification in the normal distribution proposed by Elal-Olivero adds a skewness parameter called Alpha-skew-normal (ASN) distribution, which enables bimodality and fat-tail, if needed, although it is sometimes not trivial to estimate this third parameter (regardless of the location and scale). This work analyzed seven different statistical inferential methods towards the ASN distribution on synthetic data and historical data of water flux from 21 rivers (channels) in the Atacama region. Moreover, the contributions of this paper are related to the estimations of probability surrounding rivers’ flux levels in the surroundings of Copiapó city, which is the most economically important city of the third Chilean region and is known to be located in one of the driest areas on Earth (excluding the North and the South Poles). The results show the competitiveness of the MPS and RADE methods with respect to the MLE method, as well as their excellent performance.

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Section: Introductionmentioning

confidence: 99%

Generalizing Normality: Different Estimation Methods for Skewed Information

et al. 2021

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“…Therefore, complexity is intrinsic to massive data where high-dimension is often presented, and dynamic [18,30]. Nonetheless, all the information contained in the acquired data can be extracted through an estimation method, i.e., in maximum likelihood estimation (MLE), and in a parametric version, it will be supported by a supposed distribution.…”

Section: Introductionmentioning

confidence: 99%

Generalizing the normality: a novel towards different estimation methods for skewed information

Nascimento¹,

Ramos²,

Elal-Olivero³

et al. 2021

Preprint

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Normality is the most often mathematical supposition used in data modeling. Nonetheless, even based on the law of large numbers (LLN), normality is a strong presumption given that the presence of asymmetry and multi-modality in real-world problems is expected. Thus, a flexible modification in the Normal distribution proposed by Elal-Olivero [12] adds a skewness parameter, called Alpha-skew Normal (ASN) distribution, enabling bimodality and fat-tail, if needed, although sometimes not trivial to estimate this third parameter (regardless of the location and scale). This work analyzed seven different statistical inferential methods towards the ASN distribution on synthetic data and historical data of water flux from 21 rivers (channels) in the Atacama region. Moreover, the contribution of this paper is related to the probability estimation surrounding the rivers' flux level in Copiapó city neighborhood, the most important economic city of the third Chilean region, and known to be located in one of the driest areas on Earth, besides the North and the South Pole.

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“…In addition, we adopt results about the multiregression dynamic model (MDM) (Queen and Smith, 1993;Queen, Wright and Albers, 2008;Queen and Albers, 2009) to incorporate timeseries data in the composite model. Motivation of this approach has similarities with the integrating decision support systems (IDSS) of Leonelli and Smith (2015) and Leonelli, Riccomagno and Smith (2020).…”

mentioning

confidence: 99%

A Bayesian Decision Support System in Energy Systems Planning

Volodina¹,

Sonenberg²,

Challenor³

et al. 2022

Preprint

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Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recently progress has been made in the theory of the analysis of networks of connected GP emulators. In this paper, we combine these recent methodological advances with classical state-space models to construct a Bayesian decision support system. This approach gives a coherent probability model that produces predictions with the measure of uncertainty in terms of two first moments and enables the propagation of uncertainty from individual decision components.This methodology is used to produce a decision support tool for a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. In particular, we demonstrate how to couple information from an energy model, a heating demand model, and gas and electricity price time-series to quantitatively assess the impact on operational costs of various policy choices and changes in the energy market.

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Coherent combination of probabilistic outputs for group decision making: an algebraic approach

Cited by 5 publications

References 39 publications

Generalizing Normality: Different Estimation Methods for Skewed Information

Generalizing Normality: Different Estimation Methods for Skewed Information

Generalizing the normality: a novel towards different estimation methods for skewed information

A Bayesian Decision Support System in Energy Systems Planning

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