State-population monotonicity is a property of apportionment methods, which are methods of allocating seats in a parliament among federal states. The property says that, if the population of a state increases faster than that of other states, then it should not lose a seat. An apportionment method that fails to satisfy this property is said to have a population paradox. In the apportionment literature, this property is simply called population monotonicity. However, the term "population monotonicity" is more commonly used to denote a very different property of resource-allocation rules: * In resource allocation, the property relates to the set of agents participating in the division process. A population-increase means that the previously-present agents are entitled to fewer items, as there are more mouths to feed. See population monotonicity for more information. * In apportionment, the property relates to the population of an individual state, which determines the state's entitlement. A population-increase means that a state is entitled to more seats. The parallel property in fair division is called weight monotonicity: when the "weight" (- entitlement) of an agent increases, his utility should not decrease. There are several variants of the state-population monotonicity (PM); see mathematics of apportionment for definitions and notation. (Wikipedia).
Populations, Samples, Parameters, and Statistics
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From playlist Statistics
The Normal Distribution (1 of 3: Introductory definition)
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From playlist The Normal Distribution
Clustering 1: monothetic vs. polythetic
Full lecture: http://bit.ly/K-means The aim of clustering is to partition a population into sub-groups (clusters). Clusters can be monothetic (where all cluster members share some common property) or polythetic (where all cluster members are similar to each other in some sense).
From playlist K-means Clustering
Clustering 2: Monothetic vs polythetic
From playlist Clustering Algorithms
Statistics: Ch 7 Sample Variability (1 of 14) Why Do We Sample the Population?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn what is sample variability any why do we sample the population. Some populations are very large and a sample can repre
From playlist STATISTICS CH 7 SAMPLE VARIABILILTY
Apportionment: The Population Paradox
The video explains the population paradox and provides an example of the population paradox. Site: http://mathispower4u.com
From playlist Apportionment
Lesson: Calculate a Confidence Interval for a Population Proportion
This lesson explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals
This video introduces the model used to consider migration between populations with differing allele frequencies. The equation for the mainland:island model is derived and the the analogous equation for a group of islands is provided. The equations demonstrate that the net long term effect
From playlist TAMU: Bio 312 - Evolution | CosmoLearning Biology
Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
https://www.patreon.com/ProfessorLeonard Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
From playlist Statistics (Full Length Videos)
Moshe Goldstein: "Correlation induced band competition in oxide interfaces: (001) vs. (111) LAO/STO"
Theory and Computation for 2D Materials "Correlation induced band competition in oxide interfaces: (001) vs. (111) LAO/STO" Moshe Goldstein, Tel Aviv University Abstract: The interface between the two insulating oxides SrTiO3 and LaAlO3 gives rise to a two-dimensional electron system wit
From playlist Theory and Computation for 2D Materials 2020
Mathematical Biology. 08: Phase Diagrams
UCI Math 113B: Intro to Mathematical Modeling in Biology (Fall 2014) Lec 08. Intro to Mathematical Modeling in Biology: Phase Diagrams View the complete course: http://ocw.uci.edu/courses/math_113b_intro_to_mathematical_modeling_in_biology.html Instructor: German A. Enciso, Ph.D. Textbook
From playlist Math 113B: Mathematical Biology
Evolutionary pathways to antibiotic resistance by Joachim Krug
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (ONLINE) ORGANIZERS: Debashish Chowdhury (IIT Kanpur), Ambarish Kunwar (IIT Bombay) and Prabal K Maiti (IISc, Bengaluru) DATE: 07 December 2020 to 18 December 2020 VENUE: Online 'Fluctuation-and-noise' are theme
From playlist Statistical Biological Physics: From Single Molecule to Cell (Online)
Yves Achdou: Numerical methods for mean field games - Monotone finite difference schemes
Abstract: Recently, an important research activity on mean field games (MFGs for short) has been initiated by the pioneering works of Lasry and Lions: it aims at studying the asymptotic behavior of stochastic differential games (Nash equilibria) as the number n of agents tends to infinity.
From playlist Numerical Analysis and Scientific Computing
Levon Nurbekyan: "Computational methods for nonlocal mean field games with applications"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "Computational methods for nonlocal mean field games with applications" Levon Nurbekyan - University of California, Los Angeles (UCLA) Abstract: We introduce a novel framework to model and solve me
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Two-dimensional random field Ising model at zero temperature - Jian Ding
Analysis Seminar Topic: Two-dimensional random field Ising model at zero temperature Speaker: Jian Ding Affiliation: The Wharton School, The University of Pennsylvania Date: April 5, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
The Protein Hourglass: First Passage Time Distributions for Protein Thresholds by Dibyendu Das
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (ONLINE) ORGANIZERS: Debashish Chowdhury (IIT Kanpur), Ambarish Kunwar (IIT Bombay) and Prabal K Maiti (IISc, Bengaluru) DATE: 07 December 2020 to 18 December 2020 VENUE: Online 'Fluctuation-and-noise' are themes tha
From playlist Statistical Biological Physics: From Single Molecule to Cell (Online)
Zhongyang Li: "XOR Ising model and constrained percolation"
Asymptotic Algebraic Combinatorics 2020 "XOR Ising model and constrained percolation" Zhongyang Li - University of Connecticut Abstract: I will discuss the percolation properties of the critical and non-critical XOR Ising models in the 2D Euclidean plane and in the hyperbolic plane, whos
From playlist Asymptotic Algebraic Combinatorics 2020
Percolation of Level-Sets of the Gaussian Free Field (Lecture-2) by Subhajit Goswami
PROGRAM: PROBABILISTIC METHODS IN NEGATIVE CURVATURE (ONLINE) ORGANIZERS: Riddhipratim Basu (ICTS - TIFR, Bengaluru), Anish Ghosh (TIFR, Mumbai) and Mahan M J (TIFR, Mumbai) DATE & TIME: 01 March 2021 to 12 March 2021 VENUE: Online Due to the ongoing COVID pandemic, the meeting will
From playlist Probabilistic Methods in Negative Curvature (Online)
Covariance (3 of 17) Population vs Sample Variance
Visit http://ilectureonline.com for more math and science lectures! To donate:a http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn the difference and calculate the variance of a population and the variance of a sample of a population. Next video in
From playlist COVARIANCE AND VARIANCE
Statistical Physics of Biological Evolution by Joachim Krug ( Lecture - 4 )
PROGRAM BANGALORE SCHOOL ON STATISTICAL PHYSICS - X ORGANIZERS : Abhishek Dhar and Sanjib Sabhapandit DATE : 17 June 2019 to 28 June 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the tenth in the series. This is a pedagogical school, aimed at bridgin
From playlist Bangalore School on Statistical Physics - X (2019)