Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Branchini, Nicola"'
Autor:
Branchini, Nicola, Elvira, Víctor
An essential problem in statistics and machine learning is the estimation of expectations involving PDFs with intractable normalizing constants. The self-normalized importance sampling (SNIS) estimator, which normalizes the IS weights, has become the
Externí odkaz:
http://arxiv.org/abs/2406.19974
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. These maps have significant relevance for real-world challenges such as
Externí odkaz:
http://arxiv.org/abs/2312.08107
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy tails, existing AIS algorithms can provide inconsistent estimators or ex
Externí odkaz:
http://arxiv.org/abs/2310.16653
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare.
Externí odkaz:
http://arxiv.org/abs/2208.10981
Autor:
Branchini, Nicola, Elvira, Víctor
Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an extended state space using an auxiliary variable to improve inference
Externí odkaz:
http://arxiv.org/abs/2011.09317