Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 198 for “"prediction error"”.
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Estimating lower bounds for time series prediction error
Research on how to evaluate the time series prediction algorithms are relatively under investigated compared to those to develop prediction algorithms. This research presents a way to estimate lower bounds for a time series prediction error by utilizing the conditional entropy rate, which allows us …
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The role of Prediction Error in Probabilistic Associative Learning
… without the need for Prediction Error [PE] computation. Given that blocking was the main impetus for placing PE at the centre of learning theories, I critically re-evaluate other evidence for PE in learning, particularly the recent neuroimaging evidence. I conclude that …
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Cocaine Use Modulates Neural Prediction Error During Aversive Learning
… compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their …
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Nonparametric efficient estimation of prediction error for incomplete data models
Commonly accepted measures of prediction error, such as mean squared <br>error or R^2 typically fail to be identifiable with censored <br>observations. The Brier score is a loss function which is suitable for <br>the assessment of predictions made in terms of predicted probabilities <br>that are …
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Characterizing complex time-series from the scaling of prediction error
… complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction …
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Novelty, Prediction Error and Memory Encoding: Limitations of the Pimms Framework
… used to explain how novelty, or more precisely “prediction error”, boosts memory encoding. In this thesis, I explored several other phenomena in the animal and human literature that PIMMS cannot yet explain but should. PIMMS predicts that unexpected information will be better encoded than …
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Improving numerical weather prediction: error growth at the convective scale and speed
… to increase the efficiency of Numerical Weather Prediction. Because parameterizations often occupy a significant portion of the total execution time the first focus of this work is to provide a methodology to transform parameterizations into algorithms that provide the same output at a fraction …
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Effects of Priming on Subsequent Associative Memory: Testing Prediction Error and Attentional Accounts
… framework entails feedback connections carrying predictions and feedforward connections carrying error signals. Divergences of inputs from those expected are termed prediction errors (PE), and indicate the possibility of updating the model to improve future performance. Thus, learning should be …
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Fair Selective Regression
Selective regression allows for abstention from prediction when uncertainty is high, creating a tradeoff between coverage rate and prediction error. In this thesis, we consider how selective regression interacts with data that is partitioned into subgroups by a sensitive attribute. Specifically, we …
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Theory protection in human associative learning and formally representing uncertainty about novel stimuli
… possible. Theory protection differs from typical prediction error accounts of learning (e.g. Bush & Mosteller, 1951; Rescorla & Wagner, 1972; Rescorla, 2001). According to prediction error accounts, people should update existing associations (i.e. learn) most readily when the outcomes they …
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Predicting continuous hand pose from wearable EMG sensor data using transformer-based deep-learning models
… model reduced whole-hand median prediction error from 3.6° to 3.3° for a joint angle model and from 17.4° to 15.1° for a bone orientation model. Experimental results demonstrated that Transformer models outperformed LSTM models in both median and 90th-percentile prediction error, …
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Association between Reward Sensitivity and Smoking Status in Major Depressive Disorder
… along with lower learning rate and striatal prediction error signal. Further, we show that these effects do not differ between individuals with and without major depressive disorder (MDD). In addition, a negative correlation between reward sensitivity and striatal prediction error signal was …
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Correlation of predicted breeding values across environments in the presence of selection for direct and maternal breeding values
… independently in two different environments. Prediction error variances and covariances among direct and maternal BV within environments were required for the simulation. To obtain the necessary input parameters, a variety of MME coefficient matrices were created and inverted to inspect …
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Towards a mechanistic understanding of the neurobiological mechanisms underlying psychosis
… I aimed to elucidate the nature of reward prediction error aberrancies in chronic schizophrenia. There has been some evidence suggesting that schizophrenia is associated with aberrant coding of reward prediction errors during reinforcement learning. However it is unclear whether these …
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Investigating 'optimal' kriging variance estimation :analytic and bootstrap estimators
… unbiased predictors (BLUPs) and the precision of predictions obtained from them are assessed by the mean squared prediction error (MSPE), commonly termed the kriging variance.
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Altered Neural and Behavioral Associability-Based Learning in Posttraumatic Stress Disorder
… 2011) and from large value differences (i.e. prediction error; Montague, 1996; Schultz, Dayan, & Montague, 1997) in PTSD. Combat-deployed military veterans with varying levels of PTSD symptoms completed a learning task while undergoing fMRI; behavioral choices and neural activation were …
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Optimization of path based sensor spacing on a freeway segment for travel time prediction during incidents
… incidents. Accurate vehicle travel time predictions are needed during these incidents in order for roadway users to make informed trip decisions. Path based sensors are becoming a leading technology in gathering real-time travel time data. The data is used to make travel time predictions …
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Wind Turbine Parameter Calibration Using Deep Learning Approaches
… predict the damping coefficient with an average prediction error of 0.159% and the inertia coefficient with an average prediction error of 0.176%. A sensitivity analysis was done on the MLP to test how noise in the power data and the size of the training data affected the magnitude of the …
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Analysis of reliability and validity of critical power testing in the field
… through the 3-min all-out protocol. The average prediction error associated with the relationship between CP and the 3-min all-out End Power was 7%. In Study 2, values of CP derived through a conventional laboratory CP protocol were compared with those determined outdoors on a cycling track. High …
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Mechanisms of Spoken Word Recognition and Memory Encoding Studied through Competitor Priming
… Predictive Coding) suggest that computations of prediction error by comparing heard and predicted speech sounds drive the update of lexical probabilities that are crucial to word recognition. The study results indicated that MEG signals localised to the superior temporal gyrus (STG) showed …
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