Final Report: Reliability and Robustness for Fast Bayesian Inference of Complex Data

Abstract

The overall goal of this project is to develop inference methods that practitioners can trust. Importantly, these methods should be able to run quickly on modern problems of interest, and they should be able to work on complex data. That is, practitioners should be able to trust these methods as they actually work in practice. Especially in light of existing heuristics that can yield arbitrarily wrong results, an important part of reliability comes from supporting theory and evaluation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 05, 2021
Accession Number
AD1205765

Entities

People

  • Tamara A. Broderick

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Estimators
  • Gaussian Processes
  • Information Science
  • Machine Learning
  • Markov Chains
  • Monte Carlo Method
  • Probabilistic Models
  • Statistical Algorithms

Fields of Study

  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms