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Found 18 papers, 9 papers with code
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
However, generative networks only allow sampling from the parametrized distribution; for this reason, Ramesh et al. [2022] follows the common solution of adversarial training, where the generative network plays a min-max game against a "critic" network.
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models.
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
Score Matched Neural Exponential Families for Likelihood-Free Inference
Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations.
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
Here, we develop a method to make probabilistic precipitation predictions based on features that climate models can resolve well and that is not highly sensitive to the approximations used in individual models.
Computation Applications
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies.
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model.
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England
A mathematical model for the COVID-19 pandemic spread in England is presented.
Applications Physics and Society Populations and Evolution
Anomalous Platelet Transport & Fat-Tailed Distributions
The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation.
Computational Physics Biological Physics
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption.
Computation Applications
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
Bayesian Inference of Spreading Processes on Networks
Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network.
Likelihood-free inference by ratio estimation
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.
Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.
Modelling-based experiment retrieval: A case study with gene expression clustering
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
Classification and Bayesian Optimization for Likelihood-Free Inference
This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.
Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space
We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data.
