Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Olobatuyi, Kehinde"'
Autor:
Olobatuyi, Kehinde
I propose a novel approach for nonlinear Logistic regression using a two-layer neural network (NN) model structure with hierarchical priors on the network weights. I present a hybrid of expectation propagation called Variational Expectation Propagati
Externí odkaz:
http://arxiv.org/abs/2303.01540
Autor:
Olobatuyi, Kehinde
In this paper, I propose a new class of Zero-Inflated Poisson models into the family of Cluster Weighted Models (CWMs) called Zero-Inflated Poisson CWMs (ZIPCWM). ZIPCWM extends Poisson cluster weighted models and other mixture models. I propose an E
Externí odkaz:
http://arxiv.org/abs/2208.12394
Autor:
Aykroyd, Robert G., Olobatuyi, Kehinde
Reconstructing images from downsampled and noisy measurements, such as MRI and low dose Computed Tomography (CT), is a mathematically ill-posed inverse problem. We propose an easy-to-use reconstruction method based on Expectation Propagation (EP) tec
Externí odkaz:
http://arxiv.org/abs/2208.12340
Autor:
Olobatuyi, Kehinde, Ariyo, Oludare
Modeling of high-dimensional data is very important to categorize different classes. We develop a new mixture model called Multinomial cluster-weighted model (MCWM). We derive the identifiability of a general class of MCWM. We estimate the proposed m
Externí odkaz:
http://arxiv.org/abs/2208.11221
Autor:
Olobatuyi, Kehinde
Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of "Curse of dimensionality" o
Externí odkaz:
http://arxiv.org/abs/2208.01579
Autor:
Olobatuyi, Kehinde
Publikováno v:
International Journal of Statistical Distributions and Applications. Vol. 4, No. 1, 2018, pp. 6-21
For the first time, the Generalized Gamma Burr III (GGBIII) is introduced as an important model for problems in several areas such as actuarial sciences, meteorology, economics, finance, environmental studies, reliability, and censored data in surviv
Externí odkaz:
http://arxiv.org/abs/1701.00403
Autor:
Bambi, Jonas, Olobatuyi, Kehinde, Santoso, Yudi, Sadri, Hanieh, Moselle, Ken, Rudnick, Abraham, Dong, Gracia Yunruo, Chang, Ernie, Kuo, Alex
Publikováno v:
Knowledge; Sep2024, Vol. 4 Issue 3, p444-461, 18p
Autor:
Bambi, Jonas, Dong, Gracia Yunruo, Santoso, Yudi, Moselle, Ken, Dugas, Sophie, Olobatuyi, Kehinde, Rudnick, Abraham, Chang, Ernie, Kuo, Alex
Publikováno v:
Knowledge; Jun2024, Vol. 4 Issue 2, p252-264, 13p
Autor:
Bambi, Jonas, Santoso, Yudi, Sadri, Hanieh, Moselle, Ken, Rudnick, Abraham, Robertson, Stan, Chang, Ernie, Kuo, Alex, Howie, Joseph, Dong, Gracia Yunruo, Olobatuyi, Kehinde, Hajiabadi, Mahdi, Richardson, Ashlin
Publikováno v:
BioMedInformatics; Jun2024, Vol. 4 Issue 2, p946-965, 20p
Publikováno v:
International Journal of Data Science & Analytics; Apr2024, Vol. 17 Issue 3, p261-273, 13p