Results 221 to 230 of about 2,348,869 (282)

The impact of stability considerations on genetic fine-mapping. [PDF]

Elife
Aw AJ, Jin LC, Ioannidis N, Song YS.
europepmc   +1 more source

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

Advanced Functional Materials, EarlyView.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni   +3 more
wiley   +1 more source

Misperception, self-reported probabilities and long-term care insurance take-up in the United States. [PDF]

Int J Health Econ Manag
Blavet T, Chopard B, Rapp T, Sicsic J.
europepmc   +1 more source

Sequential analysis and its applications to neuromorphic engineering. [PDF]

Front Neurosci
Mani S, Afshar S, Monk T.
europepmc   +1 more source

Economic shocks, food insufficiency and mental health: Evidence from the COVID-19 pandemic. [PDF]

PLoS One
Pan Y, Fan L, Goetz S.
europepmc   +1 more source

Nonextensive Statistics in Nanoscopic Quantum Dots. [PDF]

Nanomaterials (Basel)
Gil-Corrales JA, Morales AL, Duque CA.
europepmc   +1 more source

Epidemic Mean-Field Thresholds of SIS Dynamics on Temporal Multiplex Networks with Activity-Driven Layers

Marjani M, Chatterjee S, Bhattacharyya S, Scoglio C.   +3 more
europepmc   +1 more source

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Probability and Statistics

2021
This chapter aims to provide the fundamentals of probability and statistics. This is an essential topic, and most of the advanced topics and majority of the applied examples are rooted in probabilistic-based simulations. First the differences between discrete and continuous random variables are explained.
Victor E. Saouma, M. Amin Hariri-Ardebili   +1 more
  +4 more sources

Probability and Statistics

Nature, 1960
IF k 1(x) is any non-negative function in L 1 (− ∞, + ∞), let us write: for n = 2, 3, … Then the function: is defined almost everywhere (although it is possibly infinite for some, or all, x).
F. N. David, Ulf Grenander
openaire   +2 more sources