# Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework

arXiv: https://arxiv.org/abs/2410.01011 · HTML: https://mlsanity.com/paper/2410.01011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Masked Autoregressive Flow for Density Estimation](https://mlsanity.com/paper/1705.07057/) (2017)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [Neural Jump Stochastic Differential Equations](https://mlsanity.com/paper/1905.10403/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [RNADE: The real-valued neural autoregressive density-estimator](https://mlsanity.com/paper/1306.0186/) (2013)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Neural Point Process for Learning Spatiotemporal Event Dynamics](https://mlsanity.com/paper/2112.06351/) (2021)
- [Artificial Neural Networks Applied to Taxi Destination Prediction](https://mlsanity.com/paper/1508.00021/) (2015)
- [Automatic Integration for Spatiotemporal Neural Point Processes](https://mlsanity.com/paper/2310.06179/) (2023)
- [DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation](https://mlsanity.com/paper/1909.05637/) (2019)
- [Urban Anomalies: A Simulated Human Mobility Dataset with Injected Anomalies](https://mlsanity.com/paper/2410.01844/) (2024)
- [Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach](https://mlsanity.com/paper/1907.03381/) (2019)
- [Destination Prediction Based on Partial Trajectory Data](https://mlsanity.com/paper/2004.07473/) (2020)
- [Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2209.01559/) (2022)
- [The Patterns of Life Human Mobility Simulation](https://mlsanity.com/paper/2410.00185/) (2024)
- [Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder](https://mlsanity.com/paper/2003.08731/) (2020)
- [STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation](https://mlsanity.com/paper/2006.09892/) (2020)
- [Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories](https://mlsanity.com/paper/2409.18427/) (2024)
- [RioBusData: Outlier Detection in Bus Routes of Rio de Janeiro](https://mlsanity.com/paper/1601.06128/) (2016)
- [AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm](https://mlsanity.com/paper/2212.02645/) (2022)

## Built on by (most cited first, 2 of 2)

- [BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data](https://mlsanity.com/paper/2510.12076/) (2025)
- [TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond](https://mlsanity.com/paper/2605.06906/) (2026)

## Abstract (arXiv)

Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks to model the underlying multivariate distributions from sparse and complex datasets. Unlike traditional models, DeepBayesic is designed to manage heterogeneous inputs, accommodating both continuous and categorical data to provide a more comprehensive understanding of mobility patterns. The framework features customized neural density estimators and hybrid architectures, allowing for flexibility in modeling diverse feature distributions and enabling the use of specialized neural networks tailored to different data types. Our approach also leverages agent embeddings for personalized anomaly detection, enhancing its ability to distinguish between normal and anomalous behaviors for individual agents. We evaluate our approach on several mobility datasets, demonstrating significant improvements over state-of-the-art anomaly detection methods. Our results indicate that incorporating personalization and advanced sequence modeling techniques can substantially enhance the ability to detect subtle and complex anomalies in spatiotemporal event sequences.