7,907 research outputs found
Wright-Fisher, a probabilistic model for population genetics
No full textreservedPartendo da un’introduzione alla genetica di popolazione, argomento fondamentale per comprendere la terminologia e gli obiettivi del modello studiato, si introducono i processi markoviani e i principi alla base di essi. Su questi infatti si basa il modello di Wright-Fisher, fulcro della nostra dissertazione. Studieremo il modello dal punto di vista principalmente matematico, per poi cercare di rispondere ad alcune domande sul comportamento da aspettarsi in situazioni limite. Ne presenteremo anche alcune varianti, ottenute aggiungendo elementi specifici come la selezione, la mutazione o la migrazione degli individui della popolazione sotto esame.
Concluderemo con un’introduzione ad un modello simile a quello di Wright-Fisher, il modello di Moran, che, a differenza del primo, prevede la sovrapposizione delle generazioni, cioè si basa su una diversa modalità nella generazione di un nuovo individuo all’interno della popolazione
Coalescence and duality for Wright-Fisher and Lambda-Fleming-Viot's processes
No full textreservedCi sono diversi modi per analizzare dinamiche stocastiche legate alla genetica. Ponendosi all'interno dell'ambito dei processi di Markov e dei problemi di martingala è possibile, partendo da una popolazione dove il numero di individui rimane costante nelle generazioni, calcolare la frequenza con cui si presenteranno diverse caratteristiche all’interno di un campione o, in alternativa, conoscendo le proprietà genetiche e la dinamica del modello, studiarne la genealogia.
La discussione, passando per digressioni che nascono in maniera naturale, ad esempio la possibilità di mutazione, di selezione o la presenza di infiniti alleli, si concentra specialmente nel considerare processi stocastici che sorgono quando il numero di individui tende ad infinito. Si derivano quindi l'interpretazione e le proprietà matematiche dei processi di Wright-Fisher, Lambda-Fleming-Viot, e dei relativi processi coalescenti, con enfasi particolare sulle relazioni di dualità che vi sussistono
Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
No full text<p>Publication: Wright AM and Hillis DM (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLOS ONE.</p
-type GaAs
No full textThe electron mobility in p-type GaAs, mu-e(p), has been determined as a function of temperature by measuring the common-emitter cutoff frequency, f(T), of an AlGaAs/GaAs n-p-n heterojunction bipolar transistor. The base was 0.6-mu-m thick and it was doped with 4 X 10(18) cm-3 Be. The 300 K value of 1055 cm2/V s and 79 K value of 5000 cm2/V s for mu-e(p) are comparable to the previously measured values. The discrepancy with the calculated values is pointed out. The recombination lifetime is also measured as a function of temperature for minority carriers. The results agree reasonably well with the calculated radiative recombination time.We wish to acknowledge Keith Jenkins, Ali Ghiasi Richard Kiehl, David Frank, and Paul Solomon for technical discussions. We also acknowledge support from the IBM SUR contract at the University of Minnesota
Simulation files and results without missing data
No full text<p><strong>Publication:</strong> Wright AM and Hillis DM (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLOS ONE.</p>
<p><strong>Contents:</strong> Data sets without missing data, and the phylogenetic trees estimated from these sets.</p>
<p>Details: These data sets were simulated along the tree in Fig. 1 of the paper. No missing data distribution was imposed on these data sets.</p
Scripts
No full text<p><strong>Publication:</strong> Wright AM and Hillis DM (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLOS ONE.</p>
<p><strong>Contents:</strong> Scripts to replicate simulations and analysis.</p>
<p><strong>Details:</strong> Python and R scripts to replicate analysis. Metadata in the first line of each file and in function documentation explains script usage.</p
Simulation Tree
No full text<p><strong>Publication:</strong> Wright AM and Hillis DM (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLOS ONE.</p>
<p><strong>Contents:</strong> Tree along which the data were simulated.</p>
<p><strong>Details:</strong> Newick tree used for simulations of data sets. Tree from Pyron RA (2011). Divergence time estimation using fossils as terminal taxa and the originsof Lissamphibia. Syst Biol 60: 466–481. DOI:10.1093/sysbio/syr047</p
Management research and the future of the corporation: a new agenda
No full textWe continue the discussion in previous AMP articles and editorials on how management research could be rethought to tackle the big issues of the 21st century (Adler, 2016; Phan, Siegel & Wright, 2016). These articles call for a strengthening of the link between producers and consumers of research and for authors to ‘wrestle with the policy implications of their ideas’ (Wright & Phan, 2017)
Raw Character Data
No full text<p><strong>Publication:</strong> Wright AM and Hillis DM (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLOS ONE.</p>
<p><strong>Contents:</strong> Raw character sets used for study.</p>
<p><strong>Details:</strong> These data sets were simulated along the tree in Fig. 1 of the paper, and contain 350 characters. Data sets labels as containing rate heterogeneity contain characters that vary in rate of evolution. Data sets not labeled as such have one single rate of evolution across the data set.</p
The frequency-dependent Wright-Fisher model: diffusive and non-diffusive approximations
No full textABSTRACT. We study a class of processes that are akin to the Wright-Fisher model, with transition prob-abilities weighted in terms of the frequency-dependent fitness of the population types. By considering an approximate weak formulation of the discrete problem, we are able to derive a corresponding continuous weak formulation for the probability density. Therefore, we obtain a family of partial differential equations (PDE) for the evolution of the probability density, and which will be an approximation of the discrete process in the joint large population, small time-steps and weak selection limit. If the fitness functions are sufficiently regu-lar, we can recast the weak formulation in a more standard formulation, without any boundary conditions, but supplemented by a number of conservation laws. The equations in this family can be purely diffusive, purely hyperbolic or of convection-diffusion type, with frequency dependent convection. The particular outcome will depend on the assumed scalings. The diffusive equations are of the degenerate type; using a duality approach, we also obtain a frequency dependent version of the Kimura equation without any further assumptions. We also show that the convective approximation is related to the replicator dynamics and provide some estimate of how accurate is the convective approximation, with respect to the convective-diffusion approximation. In particular, we show that the mode, but not the expected value, of the probability distribution is modelled by the replicator dynamics. Some numerical simulations that illustrate the results are also presented. Wright-Fisher process and diffusion approximations and continuous limits and replicator equation 92D15 and 92D25 and 35K57 and 35K67 and 35L65 1
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