Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Probability estimation”

Probability Estimation

H. Yan, Council Canada, Han Liang et al. · 2006

L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site

Cited by 0

Probability estimation

Abbas Rasoolzadegan A, Rohollah Alesheykh B, Mohammad Reza Meybodi C · 2014

Active rule scheduling Learning automata a b s t r a c t Active database systems (ADSs) react automatically to the occurrence of predefined events by defining a set of active rules. One of the main modules of an ADS is the rule scheduler, which has a significant impact on the effectiveness and efficiency of ADSs. During the rule scheduling process, the rule scheduler is responsible for choosing one of the activated or ready-to-be-executed rules to evaluate its condition section or execute its action section, respectively. This process continues until there is no rule to be evaluated or executed. In this research, we evaluate and compare existing rule scheduling approaches in a laboratory environment based on a three-tier architecture. There are criteria used for the evaluation and comparison of rule scheduling approaches: Average Response Time, Throughput, Response Time Standard Deviation, Time Overhead per Transaction, and CPU Utilization. The three first criteria are used to evaluate the effectiveness, and the latter two criteria are used to evaluate the efficiency of rule scheduling approaches. In this paper, a new approach, referred to as EX-SJFEsTLA, is proposed to improve the rule scheduling process, using a learning automaton. In our laboratory environment, EX-SJFEsTLA is compared with those rule scheduling approaches that are unconstrained as EX-SJFEsTLA is. Unconstrained scheduling approaches serially schedule the rules that do not have any priorities or deadlines. The results of experiments revealed that the proposed approach improved the rule scheduling process according to the evaluation criteria. & 2014 Elsevier Ltd. All rights reserved. 1.

Cited by 0

Probability estimation.

Javier Kipen (17350750), Joakim Jaldén (17350753) · Figshare · 2023

<p>We plot in this 2D-histogram the predicted peptide probability of Probeam vs Whatprot for samples that were classified correctly.</p>

Cited by 0Open access

Kernel Probability Estimation

Greg Jensen · Figshare · 2015

Demonstrations of a method for obtaining smoothed estimates of probability in binomial and multinomial time series.

Cited by 0Open access

Probability Density Estimation

Chris Bishop · 1995

Abstract Density estimation can also be applied to unlabelled data (that is data without any class labels) where it has a number of applications. In the context of neural networks it can be applied to the distribution of data in the input space as part of the training process for radial basis function networks (Section 5.9), and to provide a method for validating the outputs of a trained neural network (Bishop, 1994b). In Chapter 6, techniques for density estimation are combined with neural network models to provide a general framework for modelling conditional density functions.

Cited by 8

Probability-Based Estimation

Jobst Heitzig · arXiv (Cornell University) · 2023

We develop a theory of estimation when in addition to a sample of $n$ observed outcomes the underlying probabilities of the observed outcomes are known, as is typically the case in the context of numerical simulation modeling, e.g. in epidemiology. For this enriched information framework, we design unbiased and consistent ``probability-based'' estimators whose variance vanish exponentially fast as $n\to\infty$, as compared to the power-law decline of classical estimators' variance.

Cited by 0Open access

Hierarchical Probability Estimation

Martin Bichsel, Krystyna W. Ohnesorge · 1994

Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows theoretically and experimentally that such events are governed by a natural similarity relation which imposes subtle but highly effective a priori constraints on the meta-probability distribution, i.e. the probability distribution of probability values. The application of these constraints significantly improves the probability estimates based on the measurements. The results are applied to estimating order-1 Markov model parameters in image processing applications.

Cited by 0

Kernel Probability Estimation

Greg Jensen · Figshare · 2015

Demonstrations of a method for obtaining smoothed estimates of probability in binomial and multinomial time series.

Cited by 0Open access

Kernel Probability Estimation

Jensen, Greg · Figshare · 2015

Demonstrations of a method for obtaining smoothed estimates of probability in binomial and multinomial time series.

Cited by 0Open access

Reproducibility Probability Estimation

2013

This chapter illustrates the estimation of reproducibility probability (RP), stemming from the success probability (SP) of the one-tailed setting. Then, the adoption of the estimate of RP to directly perform statistical tests is introduced. Since the estimate of the RP represents an alternative indicator to the p-value, the relationships between these two quantities are shown. Moreover, the variability of RP estimates is taken into account: the technique for computing a confidence interval for RP is shown, leading to the evaluation of the stability of statistical test outcomes. Some statistical criteria for stability are shown and compared, most of which are based on RP estimation. The point of view of regulatory agencies on the possibility of performing one single pivotal study is shown, and it is discussed in relation with the stability criteria. All these concepts are also illustrated in this chapter for the two-tailed setting. Controlled Vocabulary Terms one-sided test; probability; Statistical significance; two-sided test

Cited by 0

Deep Probability Estimation

Sheng Liu, Aakash Kaku, Weicheng Zhu et al. · arXiv (Cornell University) · 2021

Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary classification, with the difference that the objective is to estimate probabilities rather than predicting the specific outcome. This work investigates probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models but they mostly focus on model (epistemic) uncertainty. For problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty: precipitation forecasting from radar images, predicting cancer patient survival from histopathology images, and predicting car crashes from dashcam videos. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data.

Cited by 3Open access