Results 31 to 40 of about 380,355 (349)

Probabilistic algorithm for mining frequent sequences

Lietuvos Matematikos Rinkinys, 2010
Frequent sequence mining in large volume databases is important in many areas, e.g., biological, climate, financial databases. Exact frequent sequence mining algorithms usually read the whole database many times, and if the database is large enough, then
Julija Pragarauskaitė, Gintautas Dzemyda   +1 more
doaj   +1 more source

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

NeuroImage, 2020
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter.
Gabriel Girard, Roberto Caminiti, Alexandra Battaglia-Mayer, Etienne St-Onge, Karen S. Ambrosen, Simon F. Eskildsen, Kristine Krug, Tim B. Dyrby, Maxime Descoteaux, Jean-Philippe Thiran, Giorgio M. Innocenti   +10 more
doaj   +1 more source

Sampling-based Algorithms for Optimal Motion Planning [PDF]

, 2011
During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness.
Abramowitz M, Alterovitz R., Arya S., Arya S., Balister P., Berenson D., Berenson D., Branicky MS, Branicky MS, Brooks R., Bruce J., Bry A., Canny J., Choset H., Cortes J., Emilio Frazzoli, Ferguson D., Grimmett G., Henze N., Hsu D., Hsu D., Karaman S., Karaman S., Karaman S., Karaman S., Kavraki L., Koren Y., LaValle SM, Lee DT, Likhachev M., Lin MC, Liu Y., Luders B., Munkres JR, Plaku E., Reif JH, Resnick SI, Sahimi M., Samet H., Samet H., Schrijver A., Sertac Karaman, Shkolnik A., Stentz D., Stilman M., Stoyan D., Tedrake R., Teller S., Urmson C., Wedge NA, Yershova A., Zucker M.   +51 more
core   +3 more sources

PMBA: A Parallel MCMC Bayesian Computing Accelerator

IEEE Access, 2021
Bayesian computing, including sampling probability distributions, learning graphic model, and Bayesian reasoning, is a powerful class of machine learning algorithms with such wide applications as biologic computing, financial analysis, natural language ...
Yufei Ni, Yangdong Deng, Songlin Li
doaj   +1 more source

Probabilistic Analysis of Facility Location on Random Shortest Path Metrics [PDF]

, 2019
The facility location problem is an NP-hard optimization problem. Therefore, approximation algorithms are often used to solve large instances. Such algorithms often perform much better than worst-case analysis suggests.
A Rényi, AD Flaxman, AM Frieze, CD Howard, G Cornuejols, HN Nagaraja, JM Hammersley, K Bringmann, M Shaked, R Davis, R Hassin, S Ahn, S Janson, SM Ross, T Aven   +14 more
core   +2 more sources

The Polish Practice of Probabilistic Approach in Power System Development Planning

Energies, 2020
Power systems can be analyzed using either a deterministic or a probabilistic approach. The deterministic analysis centers on studying the quantities and indicators that characterize the operating states of the power system under strictly defined ...
Maksymilian Przygrodzki, Paweł Kubek
doaj   +1 more source

Probabilistic short-term power load forecasting based on B-SCN

Energy Reports, 2022
Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision.
Yi Ning, Ruixuan Zhao, Shoujin Wang, Baolong Yuan, Yilin Wang, Di Zheng   +5 more
doaj   +1 more source

A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models [PDF]

arXiv.org, 2023
We provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting. While most inpainting algorithms require retraining with each new mask, we prove that diffusion based inpainting generalizes well
Litu Rout, Advait Parulekar, C. Caramanis, S. Shakkottai   +3 more
semanticscholar   +1 more source

Dimensionality reduction of clustered data sets [PDF]

, 2008
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant ...
Sanguinetti, G.
core   +2 more sources

A Probabilistic Method for Certification of Analytically Redundant Systems

International Journal of Applied Mathematics and Computer Science, 2015
Analytical fault detection algorithms have the potential to reduce the size, power and weight of safety-critical aerospace systems. Analytical redundancy has been successfully applied in many non-safety critical applications.
Hu Bin, Seiler Peter
doaj   +1 more source