A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
NBF combines deep learning with classical filtering for better belief tracking.
New theorems show agents need specific internal structures to perform well under uncertainty.
In this paper, we look to address the problem of estimating the dynamic direction of arrival (DOA) of a narrowband signal impinging on a sensor array from the far field. The initial estimate is made using a Bayesian compressive sensing (BCS) framework and then tracked using a Bayesian compressed sensing Kalman filter (…
This paper simplifies complex game dynamics by using a recursive representation.
This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attr…
Online DEM improves tracking of latent states in dynamic systems.
We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in partially observable environments. Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the stat…
Improved HGF networks avoid negative precision errors in volatility updates.
Hybrid Bayesian MOT uses neural networks to improve model aspects, achieving state-of-the-art performance.
Generalized belief propagation converges to optimal solutions on graphs with motifs.
Study risk sharing with Lambda VaR under diverse beliefs.
Belief propagation recovers backpropagation results.
New framework analyzes belief evolution in social networks.
We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the distribution over the latent states in a POMDP, have been modeled using recurrent neural networks and probabilistic latent variable models, …
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking. Being independent of appearance, it is possible to generalize to a large range of objects from only small amounts of data. However, shapes represented as silhouette images are challenging to mode…
Improved error correction using neural networks and belief propagation.
This thesis investigates belief propagation's performance in graphical models with loops.
FORBES learns flexible belief states for POMDPs using normalizing flows.
This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an -agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents…
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical gu…
Model captures decision-making under bounded rationality with prior beliefs and market feedback.
By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, stat…
This paper optimizes reinsurance contracts with belief differences between insurer and reinsurer.
Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single…
In this paper I empirically investigate prediction markets for binary options. Advocates of prediction markets have suggested that asset prices are consistent estimators of the "true" probability of a state of the world being realized. I test whether the market reaches a "consensus." I find little evidence for converge…
We present a macroeconomic agent-based model that combines several mechanisms operating at the same timescale, while remaining mathematically tractable. It comprises enterprises and workers who compete in a job market and a commodity goods market. The model is stock-flow consistent; a bank lends money charging interest…
New algorithm reduces communication in distributed learning by sharing compressed beliefs.
New -BP algorithm improves belief propagation for graphs with loops.
New group testing method uses Belief Propagation for accurate screening.
Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized -divergence. We term this algorithm as $α…
The paper explains stock market predictability through a model of heterogeneous beliefs.
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such marginals when the underlying graph is a tree. But it has gained its popularity as …
Study shows price bubbles can exist even with heterogeneous beliefs.
A method for eliciting expert beliefs using preferential questions and normalizing flows.
Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.
This paper presents a general framework for studying diverse beliefs in dynamic economies. Within this general framework, the characterization of a central-planner general equilbrium turns out to be very easy to derive, and leads to a range of interesting applications. We show how for an economy with log investors hold…
Recurrent networks learn beliefs from history in partially observable environments.
Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficie…
Deep belief networks can approximate any multivariate density with binary hidden units.
RL approach for target tracking with unknown dynamics and sensor control.
POLAR learns efficient data acquisition policies using pretrained belief representations.
SOLBP extends efficient inference to uncertain Bayesian networks.
This work uses SVM to identify track component failures in AC Track Circuits.
We introduce a formal language IE that is a variant of the language PAL developed in [van Benthem 2011] by adding a belief operator and a common belief operator,specializing to stochastic analysis. A constant symbol in the language denotes a stochastic process so that we can represent several financial events as formul…
Paper introduces a new framework to improve sample efficiency in POMDPs learning.
New train tracks for complex homeomorphisms found.
New method uses cluster shapes to improve track finding in particle collisions.
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome's plausibility. Information measures based on Shannon's concept of entropy include realization information, Kullback-Leibler divergenc…