scholarly journals Asymptotic insensitivity of least-recently-used caching to statistical dependency

2004 ◽  
Author(s):  
P. Jelenkovic ◽  
A. Radovanovic
Keyword(s):  
Statistical Dependency

scholarly journals Methods for Assessment of Memory Reactivation

2018 ◽  
Vol 30 (8) ◽  
pp. 2175-2209 ◽  
Author(s):  
Shizhao Liu ◽  
Andres D. Grosmark ◽  
Zhe Chen
Keyword(s):  
Statistical Methods ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Brain Regions ◽  
Weighted Distance ◽  
Memory Reactivation ◽  
Statistical Dependency ◽  
Stored Information ◽  
Robust Statistical Methods ◽  
Population Decoding

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

scholarly journals Statistical-Mathematical Modelling of Prices of Consumer Goods and Services in Lithuania

Ekonomika ◽  
1997 ◽  
Vol 42 ◽  
Author(s):  
Žilvinas Kalinauskas ◽  
Bronislava Kaminskienė ◽  
Rimantas Rudzkis
Keyword(s):  
Mathematical Modelling ◽  
National Economy ◽  
Consumer Goods ◽  
Short Term ◽  
Monthly Data ◽  
Goods And Services ◽  
Statistical Dependency ◽  
Expert Analysis ◽  
Short Term Prediction ◽  
Random Fluctuations

Monthly data on price indices of consumer goods and services as well as groups of some goods and the principal monetary indices in Lithuania are considered in this paper using methods of mathematical statistics. The main goal of this work is to construct mathematical models of the consumer price (ePI) index, fit for short-term prediction. Statistical dependency between prices and monetary indicators is investigated in the paper. Trends and seasonal components are estimated. Random fluctuations are described using autoregression models. Regressive models of prices and monetary indicators as regressors are constructed. Errors of indicator prediction using the proposed models are estimated. An expert analysis of the state of the national economy is made, taking into account changes in price, production, and unemployment indicators. Due to data inaccuracy and frequent recalculation of indicators, only a qualitative analysis was made without applying mathematical means.

scholarly journals Measuring Independence between Statistical Randomness Tests by Mutual Information

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 741
Author(s):  
Jorge Augusto Karell-Albo ◽  
Carlos Miguel Legón-Pérez  ◽  
Evaristo José Madarro-Capó  ◽  
Omar Rojas ◽  
Guillermo Sosa-Gómez
Keyword(s):  
Mutual Information ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Statistical Dependency ◽  
Nonlinear Correlations ◽  
Randomness Tests ◽  
Dependency Detection

The analysis of independence between statistical randomness tests has had great attention in the literature recently. Dependency detection between statistical randomness tests allows one to discriminate statistical randomness tests that measure similar characteristics, and thus minimize the amount of statistical randomness tests that need to be used. In this work, a method for detecting statistical dependency by using mutual information is proposed. The main advantage of using mutual information is its ability to detect nonlinear correlations, which cannot be detected by the linear correlation coefficient used in previous work. This method analyzes the correlation between the battery tests of the National Institute of Standards and Technology, used as a standard in the evaluation of randomness. The results of the experiments show the existence of statistical dependencies between the tests that have not been previously detected.

scholarly journals Sex-specific relationships between face memory and the N170 component in event-related potentials

2020 ◽  
Vol 15 (5) ◽  
pp. 587-597
Author(s):  
Hadiseh Nowparast Rostami ◽  
Andrea Hildebrandt ◽  
Werner Sommer
Keyword(s):  
Individual Differences ◽  
Memory Performance ◽  
Negative Relationship ◽  
Event Related Potentials ◽  
Brain Potentials ◽  
Face Memory ◽  
N170 Component ◽  
Statistical Dependency ◽  
Related Potentials ◽  
And Performance

Abstract At the group level, women consistently perform better in face memory tasks than men and also show earlier and larger N170 components of event-related brain potentials (ERP), considered to indicate perceptual structural encoding of faces. Here we investigated sex differences in the relationship between the N170 and face memory performance in 152 men and 141 women at group mean and individual differences levels. ERPs and performance were measured in separate tasks, avoiding statistical dependency between the two. We confirmed previous findings about superior face memory in women and a—sex-independent—negative relationship between N170 latency and face memory. However, whereas in men, better face memory was related to larger N170 components, face memory in women was unrelated with the amplitude or latency of the N170. These data provide solid evidence that individual differences in face memory within men are at least partially related to more intense structural face encoding.

scholarly journals On a Class of Tensor Markov Fields

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 451
Author(s):  
Enrique Hernández-Lemus
Keyword(s):  
Graphical Models ◽  
Random Fields ◽  
Markov Random Fields ◽  
Random Variables ◽  
Probability Measures ◽  
Gibbs Fields ◽  
Markov Random ◽  
Statistical Dependency ◽  
Markov Fields ◽  
Dependency Structures

Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables. By extending the results of Dobruschin, Hammersley and Clifford to such tensor valued fields, we proved that tensor Markov fields are indeed Gibbs fields, whenever strictly positive probability measures are considered. Hence, there is a direct relationship with many results from theoretical statistical mechanics. We showed how this class of Markov fields it can be built based on a statistical dependency structures inferred on information theoretical grounds over empirical data. Thus, aside from purely theoretical interest, the Tensor Markov Fields described here may be useful for mathematical modeling and data analysis due to their intrinsic simplicity and generality.

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