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DTSTAMP:20190719T085744Z
LOCATION:HG EO Nord
DTSTART;TZID=Europe/Stockholm:20190613T195000
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UID:submissions.pasc-conference.org_PASC19_sess179_post121@linklings.com
SUMMARY:PHY02 - Bayesian Parameter Inference with Stochastic Solar Dynamo 
 Models
DESCRIPTION:Poster\n\n\nPHY02 - Bayesian Parameter Inference with Stochast
 ic Solar Dynamo Models\n\nUlzega, Albert\n\nTime-series of cosmogenic radi
 onuclides stored in natural archives such as ice cores and tree rings are 
 a proxy for solar magnetic activity on multi-millennial time-scales. Radio
 nuclides data exhibit a number of interesting features such as intermitten
 t stable cycles of high periods and Grand Minima. Although a lot of effort
  has gone into the development of sound physically based stochastic solar 
 dynamo models, it is still largely unclear what are the underlying mechani
 sms that lead to the observed phenomena. Answering these questions require
 s quantitatively calibrating the models to the data and comparing performa
 nces of different models with the associated uncertainties in model parame
 ters and predictions. Bayesian statistics is a consistent framework for pa
 rameter inference where knowledge about model parameters is expressed thro
 ugh probability distributions and updated using measured data. However, Ba
 yesian inference with non-linear stochastic models can become computationa
 lly extremely expensive and it is therefore hardly ever applied. In recent
  years, sophisticated and scalable algorithms have emerged, which have the
  potential of making Bayesian inference for complex stochastic models feas
 ible. We intend to investigate the power of Approximate Bayesian Computati
 on (ABC) and Hamiltonian Monte Carlo (HMC) algorithms. We present our firs
 t inference results with stochastic solar dynamo models.
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