Results 261 to 270 of about 182,690 (327)

Random discrete probability measures based on a negative binomial process

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract A distinctive functional of the Poisson point process is the negative binomial process for which the increments are not independent but are independent conditional on an underlying gamma variable. Using a new point process representation for the negative binomial process, we generalize the Poisson–Kingman distribution and its corresponding ...
Sadegh Chegini, Mahmoud Zarepour
wiley   +1 more source

Supplementary material to "Probabilistic seismic hazard analysis using logic tree approach – Patna District (India)"

open access: green, 2019
P. Anbazhagan   +4 more
openalex   +1 more source

How to measure statistical evidence and its strength: Bayes factors or relative belief ratios?

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Both the Bayes factor and the relative belief ratio satisfy the principle of evidence and are therefore valid measures of statistical evidence. Which of these measures of evidence is more appropriate? We argue here that there are questions concerning the validity of a commonly used definition of the Bayes factor based on a mixture prior, and ...
Luai Al‐Labadi   +2 more
wiley   +1 more source

MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization

open access: yesChemistry–Methods, EarlyView.
Tandem mass spectrum to decision (MS2DECIDE) leverages decision theory and expert knowledge to aggregate the outputs of three widely used annotation tools (GNPS, Sirius, and ISDB‐LOTUS) and compute a recommendation for targeting natural products with regard to their potential novelty.
Yassine Mejri   +7 more
wiley   +1 more source

Harnessing Synergies between Combinatorial Microfluidics and Machine Learning for Chemistry, Biology, and Fluidic Design

open access: yesChemistry–Methods, EarlyView.
This review explores the synergy of combinatorial microfluidics and machine learning, highlighting their transformative impact on high‐throughput experimentation, closed‐loop reaction optimization, and autonomous platform development. Key applications in chemical synthesis, biological research, and microfluidic design are discussed, addressing ...
Suyash S. Damir   +3 more
wiley   +1 more source

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