Results 61 to 70 of about 1,212,906 (326)
From Droplet to Diagnosis: Spatio‐Temporal Pattern Recognition in Drying Biofluids
Advanced Intelligent Systems, EarlyView.This article integrates machine learning (ML) with the spatio‐temporal evolution of biofluid droplets to reveal how drying and self‐assembly encode distinctive compositional fingerprints. By leveraging textural features and interpretable ML, it achieves robust classification of blood abnormalities with over 95% accuracy.
Anusuya Pal +2 more
wiley +1 more source
Dynamics, 2023 In the cognitive and neural sciences, Bayesianism refers to a collection of concepts and methods stemming from various implementations of Bayes’ theorem, which is a formal way to calculate the conditional probability of a hypothesis being true based on ...
Luis H. Favela, Mary Jean Amon
doaj +1 more source
The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA? [PDF]
, 2014 We consider the Bayesian analysis of a few complex, high-dimensional models and show that intuitive priors, which are not tailored to the fine details of the model and the estimated parameters, produce estimators which perform poorly in situations in ...
Bickel, P. J. +3 more
core +3 more sources
Advanced Physics Research, EarlyView.Cd2SnO4 exhibits excellent thermoelectric properties with a high Seebeck coefficient, power factor, and figure of merit, surpassing Bi2Te3. It shows both positive and negative Seebeck coefficient values, making it suitable for diverse applications. Its high electrical conductivity and low thermal conductivity enhance efficiency, while its negative Hall
Adel Bandar Alruqi, Nicholas O. Ongwen
wiley +1 more source
Total Belief Theorem and Generalized Bayes' Theorem
Fusion, 2018 This paper presents two new theoretical contributions for reasoning under uncertainty: 1) the Total Belief Theorem (TBT) which is a direct generalization of the Total Probability Theorem, and 2) the Generalized Bayes' Theorem drawn from TBT.
J. Dezert, A. Tchamova, Deqiang Han
semanticscholar +1 more source
A Guide to Bayesian Optimization in Bioprocess Engineering
Biotechnology and Bioengineering, EarlyView.ABSTRACT Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation.
Maximilian Siska +5 more
wiley +1 more source
Posterior probability and fluctuation theorem in stochastic processes
, 2009 A generalization of fluctuation theorems in stochastic processes is proposed. The new theorem is written in terms of posterior probabilities, which are introduced via the Bayes theorem.
Crooks G. E. +21 more
core +1 more source
Optimal model‐based design of experiments for parameter precision: Supercritical extraction case
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
wiley +1 more source
BAT - The Bayesian Analysis Toolkit
, 2008 We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution.
Akaike +14 more
core +1 more source
Predicting cervical cancer DNA methylation from genetic data using multivariate CMMP
Canadian Journal of Statistics, EarlyView.Abstract Epigenetic modifications link the environment to gene expression and play a crucial role in tumour development. DNA methylation, in particular, is gaining attention in cancer research, including cervical cancer, the focus of this study.
Hang Zhang +5 more
wiley +1 more source