Search results for " Inference"
showing 10 items of 337 documents
Inference and prediction in bulk arrival queues and queues with service in stages
1998
This paper deals with the statistical analysis from a Bayesian point of view, of bulk arrival queues where the batch size is considered as a fixed constant. The focus is on prediction of the usual measures of performance of the system in the steady state. The probability generating function of the posterior predictive distribution of the number of customers in the system and the Laplace transform of the posterior predictive distribution of the waiting time in the system are obtained. Numerical inversion of these transforms is considered. Inference and prediction of its equivalent single queue with service in stages is also discussed.
Bayesian Modelling of Confusability of Phoneme-Grapheme Connections
2007
Deficiencies in the ability to map letters to sounds are currently considered to be the most likely early signs of dyslexia. This has motivated the use of Literate, a computer game for training this skill, in several Finnish schools and households as a tool in the early prevention of reading disability. In this paper, we present a Bayesian model that uses a student's performance in a game like Literate to infer which phoneme-grapheme connections student currently confuses with each other. This information can be used to adapt the game to a particular student's skills as well as to provide information about the student's learning progress to their parents and teachers. We apply our model to …
The repeatability and reproducibility of calibrating signals generated by Hsu-Nielsen method
2005
During the measurements of the acoustic emission signals taken both in the laboratory and industrial conditions it is necessary to carry out calibration of the measurement paths used. One of the basic calibration methods is the one invented by Hsu-Nielsen, which consists in generating an acoustic signal during breaking a scriber placed in a special head put on an automatic pencil. The paper presents the measurement results of acoustic signals taken at various structural parameters of calibrating heads, which was done to determine their influence on the repeatability of the results obtained. Moreover, the measurements were taken for ten identical, in relation to their geometrical measurement…
Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia
2018
The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015&ndash
Pleistocene allopatric differentiation followed by recent range expansion explains the distribution and molecular diversity of two congeneric crustac…
2021
AbstractPleistocene glaciations had a tremendous impact on the biota across the Palaearctic, resulting in strong phylogeographic signals of range contraction and rapid postglacial recolonization of the deglaciated areas. Here, we explore the diversity patterns and history of two sibling species of passively dispersing taxa typical of temporary ponds, fairy shrimps (Anostraca). We combine mitochondrial (COI) and nuclear (ITS2 and 18S) markers to conduct a range-wide phylogeographic study including 56 populations of Branchinecta ferox and Branchinecta orientalis in the Palaearctic. Specifically, we investigate whether their largely overlapping ranges in Europe resulted from allopatric differe…
Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_79 The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive wh…
Solving Non-Stationary Bandit Problems by Random Sampling from Sibling Kalman Filters
2010
Published version of an article from Lecture Notes in Computer Science. Also available at SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_21 The multi-armed bandit problem is a classical optimization problem where an agent sequentially pulls one of multiple arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Dynamically changing (non-stationary) bandit problems are particularly challenging because each change of the reward distributions may progressively degrade the performance of any fixed strategy. Alt…
Thompson Sampling Guided Stochastic Searching on the Line for Adversarial Learning
2015
The multi-armed bandit problem has been studied for decades. In brief, a gambler repeatedly pulls one out of N slot machine arms, randomly receiving a reward or a penalty from each pull. The aim of the gambler is to maximize the expected number of rewards received, when the probabilities of receiving rewards are unknown. Thus, the gambler must, as quickly as possible, identify the arm with the largest probability of producing rewards, compactly capturing the exploration-exploitation dilemma in reinforcement learning. In this paper we introduce a particular challenging variant of the multi-armed bandit problem, inspired by the so-called N-Door Puzzle. In this variant, the gambler is only tol…
Effects of Grade Retention Policies: A Literature Review of Empirical Studies Applying Causal Inference
2021
The identification of the causal effects of grade retention policies is of enormous relevance for researchers and policymakers alike. Taking advantage of the availability of more detailed longitudinal datasets, researchers have been able to apply different identification strategies that address the classical problems of selection bias and unobserved heterogeneity that have plagued previous studies on the effect of retention. We present a systematic literature review of empirical studies aiming to unveil the causal effects of retention. This study underlines the need to consider and evaluate different kinds of grade retention polices as their effects vary depending on several dimensions (suc…
Bayesian Analysis of a Future Beta Decay Experiment's Sensitivity to Neutrino Mass Scale and Ordering
2021
Bayesian modeling techniques enable sensitivity analyses that incorporate detailed expectations regarding future experiments. A model-based approach also allows one to evaluate inferences and predicted outcomes, by calibrating (or measuring) the consequences incurred when certain results are reported. We present procedures for calibrating predictions of an experiment's sensitivity to both continuous and discrete parameters. Using these procedures and a new Bayesian model of the $\beta$-decay spectrum, we assess a high-precision $\beta$-decay experiment's sensitivity to the neutrino mass scale and ordering, for one assumed design scenario. We find that such an experiment could measure the el…