scholarly journals Comparing probabilistic accounts of probability judgments

2021 ◽  
Author(s):  
Derek Powell
Keyword(s):  
Cognitive Science ◽  
Model Comparison ◽  
Predictive Accuracy ◽  
Information Criteria ◽  
Model Complexity ◽  
Analysis Framework ◽  
Informative Priors ◽  
Probability Judgments ◽  
Bayesian Data Analysis ◽  
Bayesian Cognitive Science

Bayesian theories of cognitive science hold that cognition is fundamentally probabilistic, but people’s explicit probability judgments often violate the laws of probability. Two recent proposals, the “Probability Theory plus Noise” (Costello & Watts, 2014) and “Bayesian Sampler” (Zhu et al., 2020) theories of probability judgments, both seek to account for these biases while maintaining that mental credences are fundamentally probabilistic. These theories fit quite differently into the larger project of Bayesian cognitive science, but their many similarities complicate comparisons of their predictive accuracy. In particular, comparing the models demands a careful accounting of model complexity. Here, I cast these theories into a Bayesian data analysis framework that supports principled model comparison using information criteria. Comparing the fits of both models on data collected by Zhu and colleagues (2020) I find the data are best explained by a modified version of the Bayesian Sampler model under which people may hold informative priors about probabilities.

scholarly journals Model Selection for Biological Crystallography

2018 ◽  
Author(s):  
Nathan S. Babcock ◽  
Daniel A. Keedy ◽  
James S. Fraser ◽  
David A. Sivak
Keyword(s):  
Model Comparison ◽  
Simulated Data ◽  
Complex Model ◽  
Fixed Number ◽  
Information Criteria ◽  
Model Complexity ◽  
Scattering Data ◽  
Model Type ◽  
Statistical Field ◽  
Protein X

Structural biologists have fit increasingly complex model types to protein X-ray crystallographic data, motivated by higher-resolving crystals, greater computational power, and a growing appreciation for protein dynamics. Once fit, a more complex model will generally fit the experimental data better, but it also provides greater capacity to overfit to experimental noise. While refinement progress is normally monitored for a given model type with a fixed number of parameters, comparatively little attention has been paid to the selection among distinct model types where the number of parameters can vary. Using metrics derived in the statistical field of model comparison, we develop a framework for statistically rigorous inference of model complexity. From analysis of simulated data, we find that the resulting information criteria are less likely to prefer an erroneously complex model type and are less sensitive to noise, compared to the crystallographic cross-validation criterion Rfree. Moreover, these information criteria suggest caution in using complex model types and for inferring protein conformational heterogeneity from experimental scattering data.

Hysteretic Poisson INGARCH model for integer-valued time series

2017 ◽  
Vol 17 (6) ◽  
pp. 401-422 ◽  
Author(s):  
Buu-Chau Truong ◽  
Cathy WS Chen ◽  
Songsak Sriboonchitta
Keyword(s):  
Time Series ◽  
Model Comparison ◽  
Monte Carlo Sampling ◽  
Information Criteria ◽  
Model Parameters ◽  
Modelling Framework ◽  
South Wales ◽  
Proposed Model ◽  
Over Dispersion ◽  
Ingarch Model

This study proposes a new model for integer-valued time series—the hysteretic Poisson integer-valued generalized autoregressive conditionally heteroskedastic (INGARCH) model—which has an integrated hysteresis zone in the switching mechanism of the conditional expectation. Our modelling framework provides a parsimonious representation of the salient features of integer-valued time series, such as discreteness, over-dispersion, asymmetry and structural change. We adopt Bayesian methods with a Markov chain Monte Carlo sampling scheme to estimate model parameters and utilize the Bayesian information criteria for model comparison. We then apply the proposed model to five real time series of criminal incidents recorded by the New South Wales Police Force in Australia. Simulation results and empirical analysis highlight the better performance of hysteresis in modelling the integer-valued time series.

scholarly journals Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

2020 ◽  
Author(s):  
Quanxue Li ◽  
Wentao Dai ◽  
Jixiang Liu ◽  
Yi-Xue Li ◽  
Yuan-Yuan Li
Keyword(s):  
Colorectal Cancer ◽  
Transcriptional Regulation ◽  
Precision Medicine ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Therapeutic Benefit ◽  
Machine Learning Algorithms ◽  
Analysis Framework ◽  
Gene Dysregulation ◽  
Therapeutic Benefits

Abstract The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from TCGA. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation signature (4-DysReg), which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

MULTIVARIATE STAR ANALYSIS OF MONEY–OUTPUT RELATIONSHIP

2001 ◽  
Vol 5 (4) ◽  
pp. 506-532 ◽  
Author(s):  
Philip Rothman ◽  
Dick van Dijk ◽  
Philip Hans
Keyword(s):  
Granger Causality ◽  
Predictive Accuracy ◽  
Information Criteria ◽  
Smooth Transition ◽  
Correction Model ◽  
Vector Error Correction ◽  
Accuracy Test ◽  
Vector Error ◽  
Out Of Sample ◽  
Smooth Transition Autoregressive

This paper investigates the potential for nonlinear Granger causality from money to output. Using a standard four-variable linear (subset) vector error-correction model (VECM), we first show that the null hypothesis of linearity can be rejected against the alternative of smooth-transition autoregressive nonlinearity. An interesting result from this stage of the analysis is that the yearly growth rate of money is identified as one of the variables that may govern the switching between regimes. Smooth-transition VECM's (STVECM's) are then used to examine whether there is nonlinear Granger causality in the money–output relationship in the sense that lagged values of money enter the model's output equation as regressors. We evaluate this type of nonlinear Granger causality with both in-sample and out-of-sample analyses. For the in-sample analysis, we compare alternative models using the Akaike information criteria, which can be interpreted as a predictive accuracy test. The results show that allowing for both nonlinearity and for money–output causality leads to considerable improvement in model's in-sample performance. By contrast, the out-of-sample forecasting results do not suggest that money is nonlinearly Granger causal for output. They also show that, according to several criteria, the linear VECM's dominate the STVECM's. However, these forecast improvements seldomly are statistically significant at conventional levels.

Improving our understanding of Bangladesh tropical cyclone risk: decision making insights using kilometre scale numerical modelling and Bayesian data analysis

2020 ◽  
Author(s):  
Hamish Steptoe ◽  
Theo Economou ◽  
Bernd Becker
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Numerical Models ◽  
Management Strategies ◽  
Extreme Rainfall ◽  
Analysis Framework ◽  
Bayesian Data Analysis ◽  
Weather And Climate ◽  
Data Gap ◽  
Risk Assessment And Management

<p>We present results from state-of-the-art kilometre scale numerical models of tropical cyclones over Bangladesh.  We demonstrate how the latest generation of numerical models are filling the data gap in regions of the world with sparse observational networks, and compare our results to the latest generation global reanalyses.  We show how an ensemble of simulations expands our understanding of plausible events beyond our limited observations record.  Utilising this ensemble information in a Bayesian data analysis framework, we can robustly estimate prediction intervals for various parameters, such as peak wind speed or extreme rainfall, which when combined with Decision Theory and a loss function offer a coherent data-to-decision framework supporting disaster risk assessment and management strategies. We show how this decision making could be integrated into current global weather and climate forecast ensembles to provide forecasting of hazards and impacts up to 5 days ahead of an event, and in a future climate context.  We end with some thoughts on the ways this could influence the future of risk management and insurance underwriting and the challenges of working with big numerical model datasets.</p>

One stomatal model to rule them all? Evaluating competing hypotheses to regulate the exchange of carbon and water against experimental data

2020 ◽  
Author(s):  
Manon Sabot ◽  
Martin De Kauwe ◽  
Belinda Medlyn ◽  
Andy Pitman
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Model Comparison ◽  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Vapour Pressure Deficit ◽  
Global Climate Models ◽  
Model Complexity ◽  
Coupled Climate Models

<p>Nearly 2/3 of the annual global evapotranspiration (ET) over land arises from the vegetation. Yet, coupled-climate models only attribute between 22% – 58% of the annual terrestrial ET to plants. In coupled-climate models, the exchange of carbon and water between the terrestrial biosphere and the atmosphere is simulated by land-surface models (LSMs). Within those LSMs, stomatal conductance (g<sub>s</sub>) models allow plants to regulate their transpiration and carbon uptake, but most are empirically linked to climate, soil moisture availabilty, and CO<sub>2</sub>. Therefore, how and which g<sub>s</sub> schemes are implemented within LSMs is a key source of model uncertainty. This uncertainty has led to considerable investment in theory development in the recent years; multiple alternative hypotheses of optimal leaf-level regulation of gas exchange have been proposed as solutions to reduce existing model biases. However, a systematic inter-model evaluation is lacking (i.e. inter-model comparison within a single framework is needed to understand how different mechanistic assumptions across these new g<sub>s</sub> models affect plant behaviour). Here, we asked how, and under what conditions, nine novel optimal g<sub>s</sub> models differ from one another. The models were trained to match under average conditions before being subjected to: (i) a dry-down, (ii) high vapour pressure deficit, and (iii) elevated CO<sub>2</sub>. These experiments allowed us to identify the models’ specific responses and sensitivities. To further assess whether the models’ responses were realistic, we tested them against photosynthetic and hydraulic field data measured along mesic-xeric gradients in Europe and Australia. Finally, we evaluated model performance versus model complexity and the amount of information taken in by each model, which enables us to make recommendations regarding the use of stomatal conductance schemes in global climate models.</p>

scholarly journals Abundance Indices of Skipjack Tuna (Katsuwonus pelamis) from the Indonesian Drifting Gillnet Fishery in the Indian Ocean

2018 ◽  
Author(s):  
Dian Novianto ◽  
Ilham Ilham ◽  
Bram Setyadji ◽  
Chandara Nainggolan ◽  
Djodjo Suwardjo ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Comparison ◽  
Linear Models ◽  
Information Criteria ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Skipjack Tuna ◽  
Abundance Indices ◽  
Marginal Means ◽  
Over Exploitation

Skipjack tuna supports a valuable commercial fishery in Indonesia. Skipjack tuna are exploited in the Indian and Pacific Oceans with a variety of gear but drifting gillnets are a common method used by Indonesian fishers. However, despite of its importance, little information on the drifting gillnet fishery is available. This study describes a preliminary examination of the catch and effort data from the Indonesian skipjack drifting gillnet fishery. Utilizing daily landing report from 2010-2015, nominal catch per unit of effort (CPUE) data were calculated as kg/day at sea. Generalized Linear Models (GLM) were used to standardize the CPUE, using year, quarter, day at sea, and area as fixed variables. Model Goodness-of-fit and model comparison was carried out with the Akaike Information Criteria (AIC), the pseudo coefficient of determination (R2) and model validation with a residual analysis. The final estimation of abundance indices was calculated by least square means (LSMeans) or Marginal Means. The results showed that days accounted for most of the variation in CPUE, followed by year, quarter, and area. In general, there were no noticeable trends indicative of over exploitation or population depletion suggesting a sustainable fishery for Skipjack tuna in Indonesian waters.

scholarly journals Looking beyond general metrics for model comparison – lessons from an international model intercomparison study

2016 ◽  
Author(s):  
Tanja de Boer-Euser ◽  
Laurène Bouaziz ◽  
Jan De Niel ◽  
Claudia Brauer ◽  
Benjamin Dewals ◽  
...  
Keyword(s):  
Model Comparison ◽  
Hydrological Model ◽  
Root Zone ◽  
Model Development ◽  
Model Complexity ◽  
Model Intercomparison ◽  
Research Groups ◽  
International Model ◽  
Overall Performance ◽  
Intercomparison Study

Abstract. International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although comparative studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. Nash–Sutcliffe Efficiency), clear differences could be observed for specific events. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the Ourthe catchment. This intercomparison enhanced the understanding of the hydrological functioning of the catchment and, above all, helped to evaluate each model against a set of alternative models.

scholarly journals Spatial Models for Infants HIV/AIDS Incidence Using an Integrated Nested Laplace Approximation Approach

2019 ◽  
Author(s):  
Susan Nzula Mutua
Keyword(s):  
Random Effects ◽  
Model Comparison ◽  
Well Being ◽  
Laplace Approximation ◽  
Hiv Treatment ◽  
Information Criteria ◽  
Mother To Child Transmission ◽  
Child Transmission ◽  
Integrated Nested Laplace Approximation ◽  
Mother To Child

Abstract Background Kenya has made significant progress in the elimination of mother to child transmission of HIV through increasing access to HIV treatment and improving the health and well-being of women and children living with HIV. Despite this progress, broad geographical inequalities in infant HIV outcomes still exist. This study aimed at assessing the spatial distribution of HIV amongst infants, areas of abnormally high risk and associated risk factors for mother to child transmission of HIV using INLA and SPDE approach. Methods Data were obtained from the Early infant diagnosis (EID) database that is routinely collected for infants under one year for the year 2017. We performed both areal and point-reference analysis. Bayesian hierarchical Poisson models with spatially structured random effects were fitted to the data to examine the effects of the covariates on infant HIV risk. Spatial random effects were modelled using Conditional autoregressive model (CAR) and stochastic partial differential equations (SPDEs). Inference was done using Integrated Nested Laplace Approximation. Posterior probabilities for exceedance were produced to assess areas where the risk exceeds 1. The Deviance Information Criteria (DIC) selection was used for model comparison and selection. Results CAR model outperformed similar competing models in modeling and mapping HIV Relative Risk in Kenya. It had a smaller DIC among the rest (DIC = 306.36)) The SPDE model outperformed the spatial GLM model based on the DIC statistic. Highly active antiretroviral therapy (HAART) and breastfeeding were found to be negatively and positively associated with infant HIV positivity respectively [-0.125, 95% Credible Interval (Cred. Int.)= -0.348, -0.102], [0.178, 95% Cred. Int. -0.051, 0.412].Conclusion The study provides relevant strategic information required to make investment decisions for targeted high impact interventions to reduce HIV infections among infants in Kenya.

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