Results 281 to 290 of about 5,701,088 (332)

Application and design of a decision-making model in ethical dilemma for self-driving cars. [PDF]

Sci Rep
Liu G, Sheng J, Tao Z.
europepmc   +1 more source

Child mental health, homelessness, and the shelter system: evidence from Medicaid in New York City. [PDF]

Am J Epidemiol
Cassidy M, Currie J, Glied S, Howland R.
europepmc   +1 more source

Natural, vaccine-induced immunity and the probability of experiencing SARS-CoV-2 infection in a household cohort in Rio de Janeiro. [PDF]

Sci Rep
da Silva MFB, Guaraldo L, Bastos LS, Santos HFP, Espíndola OM, Resende PC, Penetra SLS, Pina-Costa A, de Moraes ICV, Calvet GA, Fuller TL, Siqueira MM, Carvalho MS, Brasil P.   +13 more
europepmc   +1 more source

Sequential Discrimination of Mixed Quantum States. [PDF]

Entropy (Basel)
Zhang JH, Zhang FL, Gao Y, Qin W, Fei SM.   +4 more
europepmc   +1 more source

Phylogenetic Estimation of branch-specific Shifts in the Tempo of Origination

Kopperud BT, Höhna S.
europepmc   +1 more source

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework

Badonyi M, Marsh JA.
europepmc   +1 more source

Sequencing meets cryptography: quadratic substitution error suppression through homotrimer redundancy

Sun J, Cribbs AP.
europepmc   +1 more source

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Logical Prior Probability

Artificial General Intelligence, 2012
A Bayesian prior over first-order theories is defined. It is shown that the prior can be approximated, and the relationship to previously studied priors is examined.
A. Demski
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The prior probability of autosomal linkage

Annals of Human Genetics, 1975
An expression is derived for the prior probability of linkage between a random trait locus and any one of m random marker loci, and this probability is computed form=1, 10, 20, 30, 50 and 100. A similar expression is derived for two trait loci, and computed for m=1, 10, 20 and 30.
Robert C. Elston, Kenneth Lange
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Prior probability and uncertainty

IEEE Transactions on Information Theory, 1971
Consider a stochastic system with output Y whose probability density (pY \mid \Lambda) is a known function of the parameter \Lambda whose true value is unknown. Our aim is to assign a prior probability density b(\Lambda) for \Lambda using all the available knowledge so that we can assess the probabilistic behavior of Y from the ...
R. Kashyap
openaire   +3 more sources