Neural Posterior Estimation with Differentiable Simulators

Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.Comment: Accepted at the ICML 2022 Workshop on Machine Learning for Astrophysic

Implications of Two Type Ia Supernova Populations for Cosmological Measurements

Recent work suggests that Type Ia supernovae (SNe) are composed of two distinct populations: prompt and delayed. By explicitly incorporating properties of host galaxies, it may be possible to target and eliminate systematic differences between these two putative populations. However, any resulting {\em post}-calibration shift in luminosity between the components will cause a redshift-dependent systematic shift in the Hubble diagram. Utilizing an existing sample of 192 SNe Ia, we find that the average luminosity difference between prompt and delayed SNe is constrained to be $(4.5 \pm 8.9)$. If the absolute difference between the two populations is 0.025 mag, and this is ignored when fitting for cosmological parameters, then the dark energy equation of state (EOS) determined from a sample of 2300 SNe Ia is biased at $\sim1\sigma$. By incorporating the possibility of a two-population systematic, this bias can be eliminated. However, assuming no prior on the strength of the two-population effect, the uncertainty in the best-fit EOS is increased by a factor of 2.5, when compared to the equivalent sample with no underlying two-population systematic. To avoid introducing a bias in the EOS parameters, or significantly degrading the measurement accuracy, it is necessary to control the post-calibration luminosity difference between prompt and delayed SN populations to better than 0.025 mag.Comment: 4 pages, 4 figures; New figures added, some old figures removed; The effect of the uncertainty in the two population model on parameter estimation discussed; Reflects version accepted for publication in Astrophys. J. Let

L'infliximab dans la maladie de Crohn, revue de la littérature (intérêt pronostique de la concentration résiduelle d'infliximab dans la maladie de Crohn, étude de cohorte de 44 malades)

TOURS-BU Médecine (372612103) / SudocSudocFranceF

La collecte du chiffre commercial au XVIIIe siècle

National audienc

Neural Posterior Estimation with Differentiable Simulators

Accepted at the ICML 2022 Workshop on Machine Learning for AstrophysicsICML 2022 Workshop on Machine Learning for Astrophysicshttps://ml4astro.github.io/icml2022/International audienceSimulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency

Neural Posterior Estimation with Differentiable Simulators

Accepted at the ICML 2022 Workshop on Machine Learning for AstrophysicsICML 2022 Workshop on Machine Learning for Astrophysicshttps://ml4astro.github.io/icml2022/International audienceSimulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency

Successful autologous stem cell transplantation in Gaucher disease patient with multiple myeloma

International audienc

Neural Posterior Estimation with Differentiable Simulators

Accepted at the ICML 2022 Workshop on Machine Learning for AstrophysicsICML 2022 Workshop on Machine Learning for Astrophysicshttps://ml4astro.github.io/icml2022/International audienceSimulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency

Neural Posterior Estimation with Differentiable Simulators

Accepted at the ICML 2022 Workshop on Machine Learning for AstrophysicsICML 2022 Workshop on Machine Learning for Astrophysicshttps://ml4astro.github.io/icml2022/International audienceSimulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency