Prior Weighting Research Articles

On the use of hindcast skill for merging NMME seasonal forecasts across the western U.S.

Abstract Multi-model ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill-based weighting schemes and explore ways to merge multi-model forecasts. First, we post-process each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest.

Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample.

There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Past work demonstrating prior overweighting has used simple, nonlinguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. We administered a linguistic version of the conditioned hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, hallucination severity, stimulus thresholds, and stimulus detection rates were collected. Data were used to fit parameters of a Hierarchical Gaussian Filter (HGF) model of perceptual inference to determine how latent perceptual states influenced task behavior. Replicating past results, higher CH rates were observed both in those with recent hallucinatory experiences as well as participants with high hallucination-proneness; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to hallucination severity. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and had lower response confidence, consistent with lower precision of sensory evidence. We replicate the finding that increased CH rates and recent hallucinations correlate with increased prior weighting using a linguistic version of the CH task. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness.

A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing

Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model were compared with the established measures of conditioned pain modulation (CPM) and temporal summation of pain (TSP) assessed by cuff algometry. Subsequent HGF-modelling and estimation of the influence of prior beliefs on perception showed that 70% of subjects had a higher reliance on nociceptive input during perception of acute pain stimuli, whereas 30% showed a stronger weighting of prior expectations over sensory evidence. There was no association between prior weighting estimates and CPM or TSP. The data demonstrates relevant individual differences in prior weighting and suggests an importance of top-down cognitive processes on pain perception. Our new psychophysical testing paradigm provides a method to identify individuals with traits suggesting greater reliance on prior expectations in pain perception, which may be a risk factor for developing chronic pain and may be differentially responsive to learning-based interventions.

WXGCB: A Clustering Prior Weighting Semi-Supervised Learning Method Based on Space Level Constraint and Mixed Variable Metrics

WXGCB: A Clustering Prior Weighting Semi-Supervised Learning Method Based on Space Level Constraint and Mixed Variable Metrics

Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility

BackgroundRecent advances in computational psychiatry have identified latent cognitive and perceptual states that predispose to psychotic symptoms. Behavioral data fit to Bayesian models have demonstrated an overreliance on priors (i.e., prior overweighting) during perception in select samples of individuals with hallucinations, corresponding to increased precision of prior expectations over incoming sensory evidence. However, the clinical utility of this observation depends on the extent to which it reflects static symptom risk or current symptom state. MethodsTo determine whether task performance and estimated prior weighting relate to specific elements of symptom expression, a large, heterogeneous, and deeply phenotyped sample of hallucinators (n = 249) and nonhallucinators (n = 209) performed the conditioned hallucination (CH) task. ResultsWe found that CH rates predicted stable measures of hallucination status (i.e., peak frequency). However, CH rates were more sensitive to hallucination state (i.e., recent frequency), significantly correlating with recent hallucination severity and driven by heightened reliance on past experiences (priors). To further test the sensitivity of CH rate and prior weighting to symptom severity, a subset of participants with hallucinations (n = 40) performed a repeated-measures version of the CH task. Changes in both CH frequency and prior weighting varied with changes in auditory hallucination frequency on follow-up. ConclusionsThese results indicate that CH rate and prior overweighting are state markers of hallucination status, potentially useful in tracking disease development and treatment response.

Noradrenergic deficits contribute to apathy in Parkinson's disease through the precision of expected outcomes.

Apathy is a debilitating feature of many neuropsychiatric diseases, that is typically described as a reduction of goal-directed behaviour. Despite its prevalence and prognostic importance, the mechanisms underlying apathy remain controversial. Degeneration of the locus coeruleus-noradrenaline system is known to contribute to motivational deficits, including apathy. In healthy people, noradrenaline has been implicated in signalling the uncertainty of expectations about the environment. We proposed that noradrenergic deficits contribute to apathy by modulating the relative weighting of prior beliefs about action outcomes. We tested this hypothesis in the clinical context of Parkinson's disease, given its associations with apathy and noradrenergic dysfunction. Participants with mild-to-moderate Parkinson's disease (N = 17) completed a randomised double-blind, placebo-controlled, crossover study with 40 mg of the noradrenaline reuptake inhibitor atomoxetine. Prior weighting was inferred from psychophysical analysis of performance in an effort-based visuomotor task, and was confirmed as negatively correlated with apathy. Locus coeruleus integrity was assessed in vivo using magnetisation transfer imaging at ultra-high field 7T. The effect of atomoxetine depended on locus coeruleus integrity: participants with a more degenerate locus coeruleus showed a greater increase in prior weighting on atomoxetine versus placebo. The results indicate a contribution of the noradrenergic system to apathy and potential benefit from noradrenergic treatment of people with Parkinson's disease, subject to stratification according to locus coeruleus integrity. More broadly, these results reconcile emerging predictive processing accounts of the role of noradrenaline in goal-directed behaviour with the clinical symptom of apathy and its potential pharmacological treatment.

An autonomous hybrid brain-computer interface system combined with eye-tracking in virtual environment

BackgroundBrain-computer interface (BCI) has become an effective human-machine interactive way. However, the performance of the traditional BCI system needs to be further improved, such as flexibility, robustness, and accuracy. We aim to develop an autonomous hybrid BCI system combined with eye-tracking for the control tasks in the virtual environment. New methodThis work developed an autonomous control strategy and proposed an effective fusion method for electroencephalogram (EEG) and eye tracking. For the autonomous control, the sliding window method was adopted to analyze the user's eye-gaze data. When the variance of eye-gaze data was less than the threshold, target recognition was triggered. EEG and eye-gaze data were synchronously collected and fused for classification. In addition, a fusion method based on particle swarm optimization (PSO) was proposed, which can find the best fusion weights to adapt to the differences of single modalities. ResultsEEG data and eye-gaze data of 15 subjects in steady-state visual evoked potentials (SSVEP) tasks were collected to evaluate the effectiveness of the hybrid BCI system. The results showed that the PSO fusion method performed best in all fusion methods. And the proposed hybrid BCI system obtained higher accuracy and information transfer rate (ITR) than the single-modality. Comparison with existing methodsThe PSO fusion method was compared with average weighting fusion, prior weighting fusion, support vector machine, decision tree, random forest, and extreme random tree. ConclusionThe proposed methods of autonomous control and dual-modal fusion can improve the flexibility, robustness and classification performance of the hybrid BCI system.

Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy

Attributed to the explosive adoption of large-span spatial structures and infrastructures as a critical damage-sensitive element, there is a pressing need to monitor cable vibration frequency to inspect the structural health. Neither existing acceleration sensor-utilized contact methods nor conventional computer vision-based photogrammetry methods have, to date, addressed the defects of lack in cost-effectiveness and compatibility with real-world situations. In this study, a state-of-the-art method based on modified convolutional neural network semantic image segmentation, which is compatible with extensively varying real-world backgrounds, is presented for cable vibration frequency remote and visual monitoring. Modifications of the underlying network framework lie in adopting simpler feature extractors and introducing class weights to loss function by pixel-wise weighting strategies. Nine convolutional neural networks were established and modified. Discrete images with varying real-world backgrounds were captured to train and validate network models. Continuous videos with different cable pixel-to-total pixel (C-T) ratios were captured to test the networks and derive vibration frequencies. Various metrics were leveraged to evaluate the effectiveness of network models. The optimal C-T ratio was also studied to provide guidelines for the parameter setting of monitoring systems in further research and practical application. Training and validation accuracies of nine networks were all reported higher than 90%. A network model with ResNet-50 as feature extractor and uniform prior weighting showed the most superior learning and generalization ability, of which the Precision reached 0.9973, F1 reached 0.9685, and intersection over union (IoU) reached 0.8226 when utilizing images with the optimal C-T ratio of 0.04 as testing set. Contrasted with that sampled by acceleration sensor, the first two order vibration frequencies derived by the most superior network from video with the optimal C-T ratio had merely ignorable absolute percentage errors of 0.41% and 0.36%, substantiating the effectiveness of the proposed method.

The Varying Zone Size Effect and Dual Variables for Entropy Maximising Models of Spatial Interaction

Prior weighted entropy functions compensating for the arbitrary partitioning by the zoning system of the origin and destination quantities in entropy maximising models of spatial interaction are presented and justified. This type of prior weighting is essential if the dual variables derived from the entropy maximising derivation of these models are to be used and interpreted as spatial prices in empirical studies. Failure to use prior weighted entropy functions taking account of varying zone sizes results in the biasing of dual variable values. Empirical illustrations of zone size biasing of house price and consumer welfare dual variable values are presented using a Herbert-Stevens model of a spatial housing market.

A BDS/GPS Combination Weighting Method Based on a Weighted DPOP

In order to improve the accuracy of BDS/GPS integrated positioning, and study on the reason of the positioning error in satellite positioning, learning that the location accuracy mainly comes from two aspects: One is the spatial geometric distribution of observation satellite, and other is the equivalent ranging error, therefore, the paper proposes a method of BDS/GPS combination weighting based on a weighted DPOP. Based on the a priori weighting, the method is combined with the geometric space distribution of satellite and the error factors of equivalent distance to adjust the weights, and it is used by the measured data of BDS/GPS dual-mode receiver to verify the method, The results show that the accuracy is higher than that the prior weighting and the posteriori weighting in the x and h directions.

Nonlinear moving horizon estimation in the presence of bounded disturbances

In this paper, we propose a new moving horizon estimator for nonlinear detectable systems. Similar to a recently proposed full information estimator, the corresponding cost function contains an additional max-term compared to more standard least-squares type approaches. We show that robust global asymptotic stability in case of bounded disturbances and convergence of the estimation error in case of vanishing disturbances can be established. Second, we show that the same results hold for a standard least-squares type moving horizon estimator, which so far has not been proven even in the full information estimation case. An additional advantage of the proposed estimators is that a suitable prior weighting appearing in the cost function can explicitly be determined offline, which is not the case in various existing approaches.

Adaptive prior weighting in generalized regression.

The prior distribution is a key ingredient in Bayesian inference. Prior information on regression coefficients may come from different sources and may or may not be in conflict with the observed data. Various methods have been proposed to quantify a potential prior-data conflict, such as Box's p-value. However, there are no clear recommendations how to react to possible prior-data conflict in generalized regression models. To address this deficiency, we propose to adaptively weight a prespecified multivariate normal prior distribution on the regression coefficients. To this end, we relate empirical Bayes estimates of prior weight to Box's p-value and propose alternative fully Bayesian approaches. Prior weighting can be done for the joint prior distribution of the regression coefficients or-under prior independence-separately for prespecified blocks of regression coefficients. We outline how the proposed methodology can be implemented using integrated nested Laplace approximations (INLA) and illustrate the applicability with a Bayesian logistic regression model for data from a cross-sectional study. We also provide a simulation study that shows excellent performance of our approach in the case of prior misspecification in terms of root mean squared error and coverage. Supplementary Materials give details on software implementation and code and another application to binary longitudinal data from a randomized clinical trial using a Bayesian generalized linear mixed model.

Genetic stock compositions and natal origin of green turtle (Chelonia mydas) foraging at Brunei Bay

Knowledge of genetics composition and growth stages of endangered green turtles, as well as the connectivity between nesting and foraging grounds is important for effective conservation. A total of 42 green turtles were captured at Brunei Bay with curved carapace length ranging from 43.8 to 102.0 cm, and most sampled individuals were adults and large juveniles. Twelve haplotypes were revealed in mitochondrial DNA control region sequences. Most haplotypes contained identical sequences to haplotypes previously found in rookeries in the Western Pacific, Southeast Asia, and the Indian Ocean. Haplotype and nucleotide diversity indices of the Brunei Bay were 0.8444±0.0390 and 0.009350±0.004964, respectively. Mixed-stock analysis (for both uninformative and informative prior weighting by population size) estimated the main contribution from the Southeast Asian rookeries of the Sulu Sea (mean ≥45.31%), Peninsular Malaysia (mean ≥17.42%), and Sarawak (mean ≥12.46%). Particularly, contribution from the Sulu Sea rookery was estimated to be the highest and lower confidence intervals were more than zero (≥24.36%). When estimating contributions by region rather than individual rookeries, results showed that Brunei Bay was sourced mainly from the Southeast Asian rookeries. The results suggest an ontogenetic shift in foraging grounds and provide conservation implications for Southeast Asian green turtles.

Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

ABSTRACTFull‐waveform inversion is an appealing technique for time‐lapse imaging, especially when prior model information is included into the inversion workflow. Once the baseline reconstruction is achieved, several strategies can be used to assess the physical parameter changes, such as parallel difference (two separate inversions of baseline and monitor data sets), sequential difference (inversion of the monitor data set starting from the recovered baseline model) and double‐difference (inversion of the difference data starting from the recovered baseline model) strategies. Using synthetic Marmousi data sets, we investigate which strategy should be adopted to obtain more robust and more accurate time‐lapse velocity changes in noise‐free and noisy environments. This synthetic application demonstrates that the double‐difference strategy provides the more robust time‐lapse result. In addition, we propose a target‐oriented time‐lapse imaging using regularized full‐waveform inversion including a prior model and model weighting, if the prior information exists on the location of expected variations. This scheme applies strong prior model constraints outside of the expected areas of time‐lapse changes and relatively less prior constraints in the time‐lapse target zones. In application of this process to the Marmousi model data set, the local resolution analysis performed with spike tests shows that the target‐oriented inversion prevents the occurrence of artefacts outside the target areas, which could contaminate and compromise the reconstruction of the effective time‐lapse changes, especially when using the sequential difference strategy. In a strongly noisy case, the target‐oriented prior model weighting ensures the same behaviour for both time‐lapse strategies, the double‐difference and the sequential difference strategies and leads to a more robust reconstruction of the weak time‐lapse changes. The double‐difference strategy can deliver more accurate time‐lapse variation since it can focus to invert the difference data. However, the double‐difference strategy requires a preprocessing step on data sets such as time‐lapse binning to have a similar source/receiver location between two surveys, while the sequential difference needs less this requirement. If we have prior information about the area of changes, the target‐oriented sequential difference strategy can be an alternative and can provide the same robust result as the double‐difference strategy.

Testable solution of the cosmological constant and coincidence problems

We present a new solution to the cosmological constant (CC) and coincidence problems in which the observed value of the CC, $\ensuremath{\Lambda}$, is linked to other observable properties of the Universe. This is achieved by promoting the CC from a parameter that must be specified, to a field that can take many possible values. The observed value of $\ensuremath{\Lambda}\ensuremath{\approx}(9.3\text{ }\text{ }\mathrm{Gyrs}{)}^{\ensuremath{-}2}$ [$\ensuremath{\approx}{10}^{\ensuremath{-}120}$ in Planck units] is determined by a new constraint equation which follows from the application of a causally restricted variation principle. When applied to our visible Universe, the model makes a testable prediction for the dimensionless spatial curvature of ${\ensuremath{\Omega}}_{\mathrm{k}0}=\ensuremath{-}0.0056({\ensuremath{\zeta}}_{\mathrm{b}}/0.5)$, where ${\ensuremath{\zeta}}_{\mathrm{b}}\ensuremath{\sim}1/2$ is a QCD parameter. Requiring that a classical history exist, our model determines the probability of observing a given $\ensuremath{\Lambda}$. The observed CC value, which we successfully predict, is typical within our model even before the effects of anthropic selection are included. When anthropic selection effects are accounted for, we find that the observed coincidence between ${t}_{\ensuremath{\Lambda}}={\ensuremath{\Lambda}}^{\ensuremath{-}1/2}$ and the age of the Universe, ${t}_{\mathrm{U}}$, is a typical occurrence in our model. In contrast to multiverse explanations of the CC problems, our solution is independent of the choice of a prior weighting of different $\ensuremath{\Lambda}$ values and does not rely on anthropic selection effects. Our model includes no unnatural small parameters and does not require the introduction of new dynamical scalar fields or modifications to general relativity, and it can be tested by astronomical observations in the near future.

Bayesian Parameter Estimation with Prior Weighting in ALT Model

This paper provides an overview of the application of Bayesian inference to accelerated life testing (ALT) models for the concrete case of estimation by Maximum of Aposteriori (MAP) method in the case of constant stress levels. It studies the Bayesian inference over the accelerated life model as presented in [9]. It suites, integrates and generalizes the particular cases presented in [12] and [13]. Towards the end, weighting of the prior information according to data is integrated. The paper also illustrates an experimental example.

A New Malaysian Quality of Life Index Based on Fuzzy Sets and Hierarchical Needs

The Malaysian Quality of Life Index (MQLI) released by the Economic Planning Unit (EPU), has led authors to search for alternative method of expressing this index. One of the limitations in MQLI computations is the failure to recognise unequal weights for each accounted component. This paper offers a new way of expressing the quality of life index using a mathematical modelling based on fuzzy sets theory and the proposed weights based on Maslow’s theory of hierarchical human needs. The indices of 11 components that were used to compute MQLI, again be gathered as a basis in expressing a new Malaysian Fuzzy Quality of Life Index (MFQLI). The new indices for each component yielded through a normalisation process prior weighting and aggregation to compose a new MFQLI. It was found that a fuzzy sets approach with the inclusion of weights based on human needs yielded a better index of quality of life than the MQLI.

Multi-criteria optimization of a regional spatially-distributed subsurface water flow model

This paper presents the multi-criteria calibration of a regional distributed subsurface water flow model for a 1400 km 2 irrigated agricultural area in the western San Joaquin Valley of California. Two global optimization algorithms were used to identify model parameters using data on spatially distributed local water table depth measurements, district-average groundwater pumping and district-average subsurface drainage data. Model parameters that were subjected to calibration included irrigation efficiency, effective drain depth and conductance, crop evapotranspiration correction coefficient, saturated hydraulic conductivity and specific yield values of coarse and fine fractions, and saturated hydraulic conductivity values defining water fluxes across domain boundaries. Using the single-objective function approach, the three measurement types were weighted into a single-objective function for global optimization purposes. Additionally, a three-objective multi-criteria optimization problem was formulated in which no prior weighting of the individual objectives was specified. The single-objective optimization approach resulted in identifiable parameters with relatively small uncertainties, however, most likely values for various optimized parameter approached the outer bounds of their physical-realistic ranges. The normalized weighting of the single-objective function approach emphasized the pumping and drainage data more than the water table depth data. In the multi-objective approach, the objective function of each measurement type was treated independently, so that no subjective preferences were assigned a priori. Within a single optimization run, a Pareto set of solutions was generated, which included the optimal results for each end-member of each of the three objective functions. The results showed a moderate trade-off between pumping and water table predictions, and a slight independence of drainage predictions from the other two measurements. The estimated Pareto set exhibited large parameter uncertainty, indicating possible model structural inadequacies. We further show that the magnitude of prediction uncertainties associated with the Pareto parameter uncertainty is large for making water table predictions, but much smaller for drainage and pumping predictions. Trade-offs between fitting shallow and deep water tables were revealed by considering additional performance criteria for model evaluation, namely BIAS and RMSE values for six water table depth groups. These results point to possible model improvements by spatially distributing some of the model parameters.

Using interindustry input-output relations as a Bayesian prior in employment forecasting models

This paper presents the results of using input-output tables as a source of Bayesian prior information in a national employment forecasting model. A Bayesian vector autoregressive (BVAR) estimation technique is used to incorporate the interindustry input-output table relationships into the labor market forecasting model. This technique requires that a simple translation of the direct use coefficients from the input-output table be used as prior weighting elements to depict the interindustry relations. The Bayesian model provides out-of-sample forecasts superior to those from unconstrained vector autoregressive, univariate autoregressive, a block recursive bvar model and a naive BVAR model based on the Minnesota random walk prior. This suggests that interindustry input-output table linkages provide useful information that can be effectively incorporated into labor market forecasting models.

The use of prior probabilities in maximum likelihood classification of remotely sensed data

The expected distribution of classes in a final classification map can be used to improve classification accuracies. Prior information is incorporated through the use of prior probabilities—that is, probabilities of occurrence of classes which are based on separate, independent knowledge concerning the area to be classified. The use of prior probabilities in a classification system is sufficiently versatile to allow (1) prior weighting of output classes based on their anticipated sizes; (2) the merging of continuously varying measurements (multispectral signatures) with discrete collateral information datasets (e.g., rock type, soil type); and (3) the construction of time-sequential classification systems in which an earlier classification modifies the outcome of a later one. The prior probabilities are incorporated by modifying the maximum likelihood decision rule employed in a Bayesian-type classifier to calculate a posteriori probabilities of class membership which are based not only on the resemblance of a pixel to the class signature, but also on the weight of the class which is estimated for the final output classification. In the merging of discrete collateral information with continuous spectral values into a single classification, a set of prior probabilities (weights) is estimated for each value which the discrete collateral variable may assume (e.g., each rock type or soil type). When maximum likelihood calculations are performed, the prior probabilities appropriate to the particular pixel are used in classification. For time-sequential classification, the prior classification of a pixel indexes a set of appropriate conditional probabilities reflecting either the confidence of the investigator in the prior classification or the extent to which the prior class identified is likely to change during the time period of interest.