Results 121 to 130 of about 2,253,559 (287)

Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure

Social Science Research Network, 2004
This paper presents a new approach to estimation and inference in panel data models with a general multifactor error structure. The unobserved factors and the individual-specific errors are allowed to follow arbitrary stationary processes, and the number
M. Pesaran
semanticscholar   +1 more source

Dynamical Inference of Simple Heteroclinic Networks

Frontiers in Applied Mathematics and Statistics, 2019
Heteroclinic networks are structures in phase space that consist of multiple saddle fixed points as nodes, connected by heteroclinic orbits as edges. They provide a promising candidate attractor to generate reproducible sequential series of metastable ...
Maximilian Voit, Hildegard Meyer-Ortmanns   +1 more
doaj   +1 more source

Pseudo-Encoded Stochastic Variational Inference [PDF]

arXiv, 2019
Posterior inference in directed graphical models is commonly done using a probabilistic encoder (a.k.a inference model) conditioned on the input. Often this inference model is trained jointly with the probabilistic decoder (a.k.a generator model). If probabilistic encoder encounters complexities during training (e.g.
arxiv  

Theory of Statistical Inference

Journal of the American Statistical Association
Statistical inference is about figuring out what data are telling us about the world. We gather data related to some question of interest and construct a model of how the data could have been generated.
Somabha Mukherjee
semanticscholar   +1 more source

Anytime Inference in Valuation Algebras [PDF]

arXiv, 2016
Anytime inference is inference performed incrementally, with the accuracy of the inference being controlled by a tunable parameter, usually time. Such anytime inference algorithms are also usually interruptible, gradually converging to the exact inference value until terminated.
arxiv  

Inference Plans for Hybrid Particle Filtering [PDF]

Proc. ACM Program. Lang., Vol. 9, No. POPL, Article 10. Publication date: January 2025
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and ...
arxiv   +1 more source

Graphical Models, Exponential Families, and Variational Inference

Found. Trends Mach. Learn., 2008
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models.
M. Wainwright, Michael I. Jordan
semanticscholar   +1 more source

Ecological inference [PDF]

Proceedings of the National Academy of Sciences, 1999
Ecological inference is the process of drawing conclusions about individual-level behavior from aggregate-level data. Recent advances involve the combination of statistical and deterministic means to produce such inferences.
openaire   +3 more sources

Compositional Inference Metaprogramming with Convergence Guarantees [PDF]

arXiv, 2019
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different probabilistic inference algorithms to different subproblems.
arxiv  

Images and inference [PDF]

Cognition, 1988
It is frequently asked whether imagery differs in a fundamental way from other forms of knowledge representation, specifically the predicative forms employed in artificial intelligence programs. Frequently suggested distinctions are pictorial versus descriptional, and analog versus digital.
openaire   +4 more sources