scholarly journals Multilevel selection as Bayesian inference, major transitions in individuality as structure learning

2019 ◽  
Vol 6 (8) ◽  
pp. 190202 ◽  
Author(s):  
Dániel Czégel ◽  
István Zachar ◽  
Eörs Szathmáry
Keyword(s):  
Bayesian Inference ◽  
Learning Theory ◽  
Model Comparison ◽  
Replicator Dynamics ◽  
Life Forms ◽  
Multilevel Selection ◽  
Evolutionary Transitions ◽  
Major Transitions ◽  
Bayesian Model Comparison ◽  
Evolutionary Transitions In Individuality

Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive combination of pre-adapted sub-solutions in the framework of learning theory, no general mathematical formalization of this analogy has been provided yet. Here we show, building on former results connecting replicator dynamics and Bayesian update, that (i) evolution of a hierarchical population under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models and (ii) evolutionary transitions in individuality, driven by synergistic fitness interactions, is equivalent to learning the structure of hierarchical models via Bayesian model comparison. These correspondences support a learning theory-oriented narrative of evolutionary complexification: the complexity and depth of the hierarchical structure of individuality mirror the amount and complexity of data that have been integrated about the environment through the course of evolutionary history.

scholarly journals Major evolutionary transitions as Bayesian structure learning

2018 ◽  
Author(s):  
Dániel Czégel ◽  
István Zachar ◽  
Eӧrs Szathmáry
Keyword(s):  
Learning Theory ◽  
Model Comparison ◽  
Structure Learning ◽  
Replicator Dynamics ◽  
Life Forms ◽  
Evolutionary Transitions ◽  
Bayesian Model Comparison ◽  
The Hierarchical Structure ◽  
Independent Replication ◽  
Evolutionary Transitions In Individuality

AbstractComplexity of life forms on Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive combination of pre-adapted subsolutions in the framework of learning theory, no general mathematical formalization of this analogy has been provided yet. Here we show, building on former results connecting replicator dynamics and Bayesian update, that (i) evolution of a hierarchical population under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models, and (ii) evolutionary transitions in individuality, driven by synergistic fitness interactions, is equivalent to learning the structure of hierarchical models via Bayesian model comparison. These correspondences support a learning theory oriented narrative of evolutionary complexification: the complexity and depth of the hierarchical structure of individuality mirrors the amount and complexity of data that has been integrated about the environment through the course of evolutionary history.

scholarly journals The levels of selection and evolution of individuality

2019 ◽  
Vol 62 (1) ◽  
pp. 51-67
Author(s):  
Eva Kamerer
Keyword(s):  
Social Evolution ◽  
Multilevel Selection ◽  
Levels Of Selection ◽  
Selection Models ◽  
Evolutionary Transitions ◽  
Major Transitions ◽  
Evolutionary Transitions In Individuality ◽  
Major Transitions In Evolution

In this article I will analyze the transfer of fitness during the major transitions in evolution and its place in the multilevel selection models. The aim of the analysis is to show how social evolution can explain the evolutionary transitions in individuality.

scholarly journals Toward major evolutionary transitions theory 2.0

2015 ◽  
Vol 112 (33) ◽  
pp. 10104-10111 ◽  
Author(s):  
Eörs Szathmáry
Keyword(s):  
Genetic Code ◽  
Eukaryotic Cell ◽  
Multilevel Selection ◽  
High Fidelity ◽  
Evolutionary Transitions ◽  
Major Transitions ◽  
Transitions Theory ◽  
Explanatory Mechanism

The impressive body of work on the major evolutionary transitions in the last 20 y calls for a reconstruction of the theory although a 2D account (evolution of informational systems and transitions in individuality) remains. Significant advances include the concept of fraternal and egalitarian transitions (lower-level units like and unlike, respectively). Multilevel selection, first without, then with, the collectives in focus is an important explanatory mechanism. Transitions are decomposed into phases of origin, maintenance, and transformation (i.e., further evolution) of the higher level units, which helps reduce the number of transitions in the revised list by two so that it is less top-heavy. After the transition, units show strong cooperation and very limited realized conflict. The origins of cells, the emergence of the genetic code and translation, the evolution of the eukaryotic cell, multicellularity, and the origin of human groups with language are reconsidered in some detail in the light of new data and considerations. Arguments are given why sex is not in the revised list as a separate transition. Some of the transitions can be recursive (e.g., plastids, multicellularity) or limited (transitions that share the usual features of major transitions without a massive phylogenetic impact, such as the micro- and macronuclei in ciliates). During transitions, new units of reproduction emerge, and establishment of such units requires high fidelity of reproduction (as opposed to mere replication).

Bayesian model comparison

2014 ◽  
pp. 101-117
Author(s):  
Michael D. Lee ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison

scholarly journals A complete search for redshift z ≳ 6.5 quasars in the VIKING survey

2020 ◽  
Vol 501 (2) ◽  
pp. 1663-1676
Author(s):  
R Barnett ◽  
S J Warren ◽  
N J G Cross ◽  
D J Mortlock ◽  
X Fan ◽  
...  
Keyword(s):  
Model Comparison ◽  
High Redshift ◽  
Detailed Comparison ◽  
Selection Function ◽  
Data Set ◽  
Bayesian Model Comparison ◽  
Fitting Method ◽  
Complete Search ◽  
Potential Contaminant ◽  
Ranked List

ABSTRACT We present the results of a new, deeper, and complete search for high-redshift 6.5 < z < 9.3 quasars over 977 deg2 of the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey. This exploits a new list-driven data set providing photometry in all bands Z, Y, J, H, Ks, for all sources detected by VIKING in J. We use the Bayesian model comparison (BMC) selection method of Mortlock et al., producing a ranked list of just 21 candidates. The sources ranked 1, 2, 3, and 5 are the four known z > 6.5 quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-χ2 SED fitting. We find that: (i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag deeper, (ii) the minimum-χ2 SED-fitting method is extremely efficient but reaches 0.7 mag less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high χ2 values, include bright examples of galaxies with very strong [O iii] λλ4959,5007 emission in the Y band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint z > 7 quasars, not previously accounted for, and that requires better characterization.

scholarly journals A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Visual Integration ◽  
Bayesian Model Comparison ◽  
Comparison Approach

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

2021 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Bayes Factors ◽  
Prior Model ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Model Comparison ◽  
Posterior Model Probability ◽  
Model Probability

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

Combined-chain nested sampling for efficient Bayesian model comparison

2017 ◽  
Vol 70 ◽  
pp. 84-93 ◽  
Author(s):  
R. Wesley Henderson ◽  
Paul M. Goggans ◽  
Lei Cao
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Nested Sampling ◽  
Bayesian Model Comparison
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