Results 71 to 80 of about 152 (143)

Adjusted Kolmogorov Complexity of Binary Words with Empirical Entropy Normalization. [PDF]

Entropy (Basel)
Vidakovic B.
europepmc   +1 more source

Aggregation and the Structure of Value

Noûs, EarlyView.
ABSTRACT Roughly, the view I call “Additivism” sums up value across time and people. Given some standard assumptions, I show that Additivism follows from two principles. The first says that how lives align in time cannot, in itself, matter. The second says, roughly, that a world cannot be better unless it is better within some period or another.
Weng Kin San
wiley   +1 more source

Khinchin Families, Set Constructions, Partitions and Exponentials. [PDF]

Mediterr J Math
Cantón A, Fernández JL, Fernández P, Maciá VJ.   +3 more
europepmc   +1 more source

Effective Matrix Designs for COVID-19 Group Testing

, 2022
Brust D, Brust JJ.
europepmc   +1 more source

Semiotic Aggregation in Deep Learning. [PDF]

Entropy (Basel), 2020
Muşat B, Andonie R.
europepmc   +1 more source

Panel Sequential Group Estimation of Interactive Effects Models

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT This paper proposes a novel procedure to identify latent groups in the slopes of panel data models with interactive effects. The method is straightforward to apply and relies only on closed‐form estimators when evaluating the objective function.
Ignace De Vos, Joakim Westerlund
wiley   +1 more source

Some Open Mathematical Problems on Fullerenes. [PDF]

J Chem Inf Model
Bille A, Buchstaber V, Spodarev E.
europepmc   +1 more source

NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization. [PDF]

Netw Neurosci, 2022
Geniesse C, Chowdhury S, Saggar M.
europepmc   +1 more source

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement. [PDF]

Sensors (Basel), 2023
Hassani S, Dackermann U.
europepmc   +1 more source

Sensitivity analysis for generalized estimating equation with non‐ignorable missing data

Scandinavian Journal of Statistics, EarlyView.
Abstract Many incomplete‐data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real‐data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed
Hui Gong, Kin Wai Chan
wiley   +1 more source