Efficient Bayesian Inference for Evidence Accumulation Models

The thesis develops efficient Bayesian estimation methods for Evidence Accumulation Models (EAMs), an important class of Cognitive Science models. Over the last twenty years, these are the dominant models of decision-making that consider decision time and are used to address important theoretical and applied questions in psychology. Most modern applications of EAMs include a hierarchical random effects structure for individual differences. Traditional Bayesian methods, based on Markov Chain Monte Carlo (MCMC), can be very costly for estimating hierarchical EAMs. Variational Bayes (VB) methods are an alternative to Bayesian MCMC methods and are increasingly used for approximate Bayesian inference in a wide range of challenging statistical models. VB methods can produce results ten or 100 times faster than exact methods such as MCMC. However, unlike MCMC, variational methods are approximate. Despite their strengths, VB methods are not widely used in psychological research. The first contribution of the thesis is to develop VB methods for the two benchmark EAMs: the linear ballistic accumulator (LBA) model and the diffusion decision model (DDM). In addition, an exact Bayesian estimation based on MCMC is developed for the DDM including random effects. Empirical studies show that, with careful choices of the approximating distribution and optimisation algorithms, VB methods can produce relatively accurate estimates of the posterior first moments much faster than MCMC. The second important contribution is to propose a novel statistical model selection technique based on VB called Cross-validation with Variational Bayes (CVVB). The CVVB method uses VB to estimate the hierarchical EAMs. It reduces the computational time significantly, making it possible to analyse and compare many complex EAMs in a reasonable time. The third contribution of the thesis is to extend both exact and approximate Bayesian estimation to EAMs with covariates. The methods developed in this thesis are applied to simulated data, and to real data from three highly cited experiments

New Method of Tilt Measurement forApplications in Medical Devices

芝浦工業大学2016年

Anharmonic EXAFS and its Parameters of HCP Crystals: Theory and Comparison to Experiment

A new theory for ab initio calculation of the anharmonic Extended X-ray Absorption Fine Structure (EXAFS) and its parameters of hcp crystals has been developed based on the single- shell model. Analytical expressions for the anharmonic contributions to the amplitude and to the phase of EXAFS and a new anharmonic factor have been derived. The EXAFS cumulant expressions are formulated based on the anharmonic correlated Einstein model. The EXAFS function and its parameters contain anharmonic effects at high temperature and appoach those of the harmonic model at low temperature. Numerical results for Zn agree well with the experimental values

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)

A METHOD OF SIDE-PEAK MITIGATION APPLIED TO BINARY OFFSET CARRIER MODULATED GNSS SIGNALS TRACKING APPLIED IN GNSS RECEIVERS

ABSTRACT In this study, a new method of signal tracking technique in Global Navigation Satellite System (GNSS) is proposed. It is based on a combination of the autocorrelation function (ACF) with another cross correlation function in order to eliminate or reduce the power of the side peaks in ACF of Binary Offset Carrier (BOC) modulated signals. These types of modulated signals are adopted by both GNSSs like the modernized Global Positioning System (GPS) and Galileo. Moreover, this method still keep the sharp of main peak of ACF in order to maintain the advantage of BOC(n,n) signals in code tracking and multipath mitigation. In the proposed method, the output of the discriminator in delay tracking loop has no false lock point. The performance of multipath mitigation of the proposed method is better than Narrow Correlator method. The good performance of the proposed scheme in multipath mitigation has been tested using simulation results