Results 1 to 10 of about 75,217 (233)

Tracking an Auto-Regressive Process with Limited Communication per Unit Time [PDF]

Entropy, 2021
Samples from a high-dimensional first-order auto-regressive process generated by an independently and identically distributed random innovation sequence are observed by a sender which can communicate only finitely many bits per unit time to a receiver ...
Rooji Jinan, Parimal Parag, Himanshu Tyagi   +2 more
doaj   +4 more sources

Adaptive Importance Sampling Via Auto-Regressive Generative Models and Gaussian Processes

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need for weight calculation, point evaluation of the proposal
Hechuan, Wang, Mónica F, Bugallo, Petar M, Djurić   +2 more
openaire   +4 more sources

Negative binomial integer-valued auto-regressive process for longitudinal count data

Journal of Statistical Research
This paper proposes a longitudinal integer-valued auto-regressive model of order one with Negative-Binomial marginals. The proposed model is suitable for analyzing repeated count data that exhibits significant over-dispersion at each time point and that is exposed to sev- eral time-dependent covariates.
NAUSHAD MAMODE KHAN, VANDNA JOWAHEER, YUVRAJ SUNECHER   +2 more
openaire   +2 more sources

Varying-Coefficient Additive Models with Density Responses and Functional Auto-Regressive Error Process

Entropy
In many practical applications, data collected over time often exhibit autocorrelation, which, if unaccounted for, can lead to biased or misleading statistical inferences. To address this issue, we propose a varying-coefficient additive model for density-
Zixuan Han, Tao Li, Jinhong You, Narayanaswamy Balakrishnan   +3 more
doaj   +3 more sources

Dissecting Recall of Factual Associations in Auto-Regressive Language Models [PDF]

Conference on Empirical Methods in Natural Language Processing, 2023
Transformer-based language models (LMs) are known to capture factual knowledge in their parameters. While previous work looked into where factual associations are stored, only little is known about how they are retrieved internally during inference.
Mor Geva, Jasmijn Bastings, Katja Filippova, A. Globerson   +3 more
semanticscholar   +1 more source

A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data

Austrian Journal of Statistics, 2021
Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the ...
Nasrin Lipi, Mohammad Samsul Alam, Syed Shahadat Hossain   +2 more
doaj   +1 more source

Shaping energy cost management in process industries through clustering and soft sensors

Frontiers in Energy Research, 2023
With the ever-increasing growth of energy demand and costs, process monitoring of operational costs is of great importance for process industries.
Yu Lu, Gang Fang, Gang Fang, Daoping Huang, Daoping Huang, Baoping Cai, Hongtian Chen, Yiqi Liu, Yiqi Liu   +8 more
doaj   +1 more source

Auto-regressive independent process analysis without combinatorial efforts [PDF]

Pattern Analysis and Applications, 2009
We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes (auto-regressive independent process analysis, AR-IPA). Independent subspace analysis (ISA) can be used to solve the AR-IPA task. The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to one ...
Zoltán Szabó, Barnabás Póczos, András Lőrincz   +2 more
openaire   +1 more source

A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times [PDF]

International Journal of Forecasting, 2023
Lead time data is compositional data found frequently in the hospitality industry. Hospitality businesses earn fees each day, however these fees cannot be recognized until later.
Harrison Katz, K. Brusch, R. Weiss
semanticscholar   +1 more source

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences [PDF]

AAAI Conference on Artificial Intelligence, 2023
Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.).
Ruichu Cai, Yuequn Liu, Wei Chen, Jie Qiao, Yuguang Yan, Zijian Li, Keli Zhang, Z. Hao   +7 more
semanticscholar   +1 more source