A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Calibrating Expert Assessments Using Hierarchical Gaussian Process Models (2020)

Perälä, T., Vanhatalo, J., & Chrysafi, A. (2020). Calibrating Expert Assessments Using Hierarchical Gaussian Process Models. Bayesian Analysis, 15(4), 1251-1280. https://doi.org/10.1214/19-BA1180

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Perälä, Tommi; Vanhatalo, Jarno; Chrysafi, Anna

Lehti tai sarja: Bayesian Analysis

ISSN: 1936-0975

eISSN: 1931-6690

Julkaisuvuosi: 2020

Volyymi: 15

Lehden numero: 4

Artikkelin sivunumerot: 1251-1280

Kustantaja: International Society for Bayesian Analysis

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1214/19-BA1180

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/73374

Tiivistelmä

Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts’ biases to vary as a function of the true value of the parameter and according to the expert’s background. We follow the fully Bayesian approach (the so-called supra-Bayesian approach) and model experts’ bias functions explicitly using hierarchical Gaussian processes. We show how to use calibration data to infer the experts’ observation models with the use of bias functions and to calculate the bias corrected posterior distributions for an unknown system parameter of interest. We demonstrate and test our model and methods with simulated data and a real case study on data-limited fisheries stock assessment. The case study results show that experts’ biases vary with respect to the true system parameter value and that the calibration of the expert assessments improves the inference compared to using uncalibrated expert assessments or a vague uniform guess. Moreover, the bias functions in the real case study show important differences between the reliability of alternative experts. The model and methods presented here can be also straightforwardly applied to other applications than our case study.

YSO-asiasanat: arviointimenetelmät; asiantuntijat; päätöksenteko; tilastolliset mallit; gaussiset prosessit; bayesilainen menetelmä; kalakantojen hoito

Vapaat asiasanat: expert elicitation; bias correction; Gaussian process; Supra Bayes; fisheries science; environmental management.

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2