Projekteja vuodessa
Abstrakti
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several socalled likelihoodfree methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution. In another popular approach called approximate Bayesian computation, the inference is performed by identifying parameter values for which the summary statistics of the simulated data are close to those of the observed data. Synthetic likelihood is easier to use as no measure of “closeness” is required but the Gaussianity assumption is often limiting. Moreover, both approaches require judiciously chosen summary statistics. We here present an alternative inference approach that is as easy to use as synthetic likelihood but not as restricted in its assumptions, and that, in a natural way, enables automatic selection of relevant summary statistic from a large set of candidates. The basic idea is to frame the problem of estimating the posterior as a problem of estimating the ratio between the data generating distribution and the marginal distribution. This problem can be solved by logistic regression, and including regularising penalty terms enables automatic selection of the summary statistics relevant to the inference task. We illustrate the general theory on canonical examples and employ it to perform inference for challenging stochastic nonlinear dynamical systems and highdimensional summary statistics.
Alkuperäiskieli  Englanti 

Sivumäärä  31 
Julkaisu  Bayesian Analysis 
Vuosikerta  17 
Numero  1 
Varhainen verkossa julkaisun päivämäärä  2021 
DOI  pysyväislinkit  
Tila  Julkaistu  maaliskuuta 2022 
OKMjulkaisutyyppi  A1 Julkaistu artikkeli, soviteltu 
Sormenjälki
Sukella tutkimusaiheisiin 'Likelihoodfree inference by ratio estimation'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
 2 Päättynyt

Interaktiivinen koneoppiminen useista biodatalähteistä
Jälkö, J., Hegde, P., Kaski, S., Gadd, C., Jain, A., Hämäläinen, A., Siren, J., Shen, Z. & Trinh, T.
01/01/2019 → 31/08/2021
Projekti: Academy of Finland: Other research funding

Interaktiivinen koneoppiminen useista biodatalähteistä
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding