Fast and flexible analysis of direct dark matter search data with machine learning
We present the results from combining machine learning with the profile likelihood fit
procedure, using data from the Large Underground Xenon (LUX) dark matter experiment …
procedure, using data from the Large Underground Xenon (LUX) dark matter experiment …
Conditional mutual information estimation for mixed, discrete and continuous data
OC Mesner, CR Shalizi - IEEE Transactions on Information …, 2020 - ieeexplore.ieee.org
Fields like public health, public policy, and social science often want to quantify the degree
of dependence between variables whose relationships take on unknown functional forms …
of dependence between variables whose relationships take on unknown functional forms …
Inferential moments of uncertain multivariable systems
K Vanslette - arXiv preprint arXiv:2305.01841, 2023 - arxiv.org
This article expands the framework of Bayesian inference and provides direct probabilistic
methods for approaching inference tasks that are typically handled with information theory …
methods for approaching inference tasks that are typically handled with information theory …
Using Monte Carlo Tree Search to Calculate Mutual Information in High Dimensions
N Carrara, J Ernst - arXiv preprint arXiv:2309.08516, 2023 - arxiv.org
Mutual information is an important measure of the dependence among variables. It has
become widely used in statistics, machine learning, biology, etc. However, the standard …
become widely used in statistics, machine learning, biology, etc. However, the standard …
[PDF][PDF] Toward Optimal Conjunction-Based Sensor Tasking using Inferential Moments
K Vanslette, R Taing - Proceedings of the Advanced Maui Optical and …, 2023 - amostech.com
We provide a framework for the automatic refining and tracking of conjunction probability
estimates, which we call conjunction-based sensor tasking. This differs from traditional …
estimates, which we call conjunction-based sensor tasking. This differs from traditional …
The Inference Framework
N Carrara - arXiv preprint arXiv:2207.08785, 2022 - arxiv.org
The following three sections and appendices are taken from my thesis" The Foundations of
Inference and its Application to Fundamental Physics" from 2021, in which I construct a …
Inference and its Application to Fundamental Physics" from 2021, in which I construct a …
[图书][B] The foundations of inference and its application to fundamental physics
N Carrara - 2021 - search.proquest.com
THE FOUNDATIONS OF INFERENCE AND ITS APPLICATION TO FUNDAMENTAL PHYSICS
Page 1 THE FOUNDATIONS OF INFERENCE AND ITS APPLICATION TO FUNDAMENTAL …
Page 1 THE FOUNDATIONS OF INFERENCE AND ITS APPLICATION TO FUNDAMENTAL …
Fast and flexible analysis of direct dark matter search data with machine learning
We present the results from combining machine learning with the profile likelihood fit
procedure, using data from the Large Underground Xenon (LUX) dark matter experiment …
procedure, using data from the Large Underground Xenon (LUX) dark matter experiment …
Non-Parametric Causal Discovery for Discrete and Continuous Data
OC Mesner - 2020 - search.proquest.com
Subject-matter experts typically think of their datasets as causes and effects between many
variables, forming a large, complex causal system. Directed acyclic graphs (DAG), also …
variables, forming a large, complex causal system. Directed acyclic graphs (DAG), also …