Statistical inference of prehistoric demography from frequency distributions of radiocarbon dates: a review and a guide for the perplexed

The last decade saw a rapid increase in the number of applications where time-frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its simple and universal premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled by challenges. Here I review: 1) the most common criticisms concerning the nature of radiocarbon time-frequency data as a demographic proxy; 2) the statistical nature of the problem; and 3) three classes of inferential approaches proposed so far in the literature.

Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection

Bayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.

Study of Copolymerization Reaction N,N-Dialylaminocarbonic Acids with Vinyl Acetate

The reaction of radical copolymerization of N, N-diallylaminocarboxylic acids with vinyl acetate in an aqueous and aqueous-organic medium (a mixture of methanol-water in a ratio of 70:30 mol.%) Obtained copolymers of a statistical nature. It was found that vinyl acetate under these conditions is more reactive than N, N-diallylaminocarboxylic acids. The kinetic laws of the reactions have been investigated and the structures of the copolymers have been established.

Inhomogeneous Superconductivity and the ‘Pseudogap’ State of Novel Superconductors

This chapter discusses the high-Tc oxides, which display many unusual properties above Tc, especially for the underdoped compounds. One can observe some features typical for the superconducting state, such as the energy gap, anomalous diamagnetism, and the isotope effect; they coexist with finite resistance. These features are caused by an intrinsic inhomogeneity of the compound. Various energy scales (Tc, Tc*, T*) can be introduced. The system contains a set of superconducting ‘islands’ embedded in a normal metallic matrix. The inhomogeneity is caused by the statistical nature of doping and the pair-breaking effect. The formation of a macroscopic superconducting phase (at T = Tc) corresponds to the transition, which is of a percolative nature. The resistive and Meissner transitions are split. The granular superconductors are inhomogeneous and their properties are similar to those of doped systems. The ordered doping should lead to an increase in the value of the critical temperature.

Statistical Treatment of the Gamma-Ray Spectrum Using the Language Programming R

In this work we use the laws of statistics and statistical distributions software to try to understand the random statistical nature of the dissolution of gamma rays and their interaction with the material by studying the spectrum regions of the sodium-22 spectrum using the statistical programming language R. such Normality test, Anova Test as well as Pearson correlation coefficient test and the linear Regression test and we were found that there is no strong statistical relationship between the random variables studied using linear regression, also it was found that the average mean of the standard samples follows the normal distribution and this was confirmed by using the T-test, there is also a slight difference between the sampling random mean and the standard mean for the same samples using the same statistical distribution.

Probabilistic strength assessment through analytical modelling of pillars found in ship structures

The compressive strength of pillars found in ship structures is studied under a reliability perspective. Monte Carlo Simulations (MCS) are applied, aided by a stratified sample scheme (i.e. Latin Hypercube), to account for uncertainty within the problem’s input variables (yield stress, elastic modulus, initial bow imperfection). MCSs were applied for three slenderness ratio values (low, medium, high), for hollow-circular, hollow-square and “H” shape cross-sections, and multiple geometries per slenderness ratio, per cross-section. The pillar’s strength is calculated based on the Perry-Robertson formula. The probabilistic resistance per pillar was modelled by generating the probability density function that best describes the statistical nature of the sample data. In this paper we illustrate the probabilistic nature of the compression column resistance, and compare it to the deterministic resistance suggested by regulatory bodies.

Study of time series characteristics by statistical properties of cryptocurrency prices and multifractal analysis

Physicist Professor Tetsuya Takaishi is based at the liberal arts education center, Hiroshima University of Economics in Japan, and is conducting research on a variety of cryptocurrencies. His research is focused on clarifying the mechanism of price fluctuations, in particular answering questions about the volatility inherent in cryptocurrencies, including understanding what affects the price of cryptocurrencies and why the price fluctuates in the way that it does. His chief focus is on cryptocurrencies, specifically Bitcoin, which is the most well-known of the cryptocurrencies. Takaishi's latest project is investigating the statistical nature of Bitcoin price fluctuations.

Universality in biodiversity patterns: Variation in species-temperature and species-productivity relationships reveals a prominent role of productivity in diversity gradients

Temperature and productivity appear as universal positive correlates of species richness. However, the strength and the shape of species-temperature (STR) and species-productivity (SPR) relationships vary widely, and the causes of this variation are poorly known. We analysed (1) published species richness data for multiple taxa sampled in various regions and (2) different clades within vertebrate classes globally, to test for the effects of spatial scale and characteristics of examined taxa and regions on the strength and direction of STRs and SPRs. There are striking differences in the variation of the relationships among types of data, between ectotherms and endotherms and also between STRs and SPRs. Some sources of this variation are of statistical nature (e.g. the relationships are stronger if the range of temperature or productivity variation is wider), but non-statistical sources are more important and illuminate the processes responsible for the origin of biodiversity patterns. The SPRs are generally stronger and less variable than STRs, and SPR variation is weakly related to the explored factors - the SPRs are stronger in warmer regions in ectotherms, while clade size is the only factor consistently affecting the strength of the SPR in endotherms. In contrast, STRs are weaker and more variable, and this variation is linked to region characteristics - most importantly, STRs are stronger in the regions where temperature positively correlates with productivity, indicating that productivity plays a role even in the STRs. The effect of temperature on species richness is thus complex and context-dependent, while productivity is a more universal driver of species richness patterns, largely independent of particular characteristics of given region or taxon. Productivity thus appears as the main proximate driver of species richness patterns, probably due to its effect on the limits of the number of viable populations which can coexist in a given environment.

The Born rule as a statistics of quantum micro-events

We deduce the Born rule from a purely statistical take on quantum theory within minimalistic math-setup. No use is required of quantum postulates. One exploits only rudimentary quantum mathematics—a linear, not Hilbert’, vector space—and empirical notion of the Statistical Length of a state. Its statistical nature comes from the lab micro-events (detector-clicks) being formalized into the C -coefficients of quantum superpositions. We also comment that not only has the use not been made of quantum axioms (scalar-product, operators, interpretations , etc.), but that the involving thereof would be, in a sense, inconsistent when deriving the rule. In point of fact, the quadratic character of the statistical length, and even not (the ‘physics’ of) Born’s formula, represents a first step in constructing the mathematical structure we name the Hilbert space of quantum states.