Happy to announce that our paper on (generalised) Bayesian online learning in non-stationary environments has been accepted at @tmlrorg.bsky.social!
arxiv.org/abs/2411.10153
arxiv.org/abs/2411.10153
March 14, 2025 at 9:43 AM
Happy to announce that our paper on (generalised) Bayesian online learning in non-stationary environments has been accepted at @tmlrorg.bsky.social!
arxiv.org/abs/2411.10153
arxiv.org/abs/2411.10153
Part II of a Bayesian’s take on (Kalman) filtering—without Bayes.
All about the basics of state-space models.
grdm.io/posts/filter...
All about the basics of state-space models.
grdm.io/posts/filter...
Filtering notes (II): state-space models
Part II of the filtering notes series:
Part I: signal plus noise models Part II: state-space-models* Introduction Whether modelling the trajectory of a rocket, forecasting economic trends, or powering...
grdm.io
March 6, 2025 at 7:36 AM
Part II of a Bayesian’s take on (Kalman) filtering—without Bayes.
All about the basics of state-space models.
grdm.io/posts/filter...
All about the basics of state-space models.
grdm.io/posts/filter...
Part I of a Bayesian’s take on (Kalman) filtering—without Bayes.
I will cover filtering and applications—from classical tracking problems to time-series forecasting, sequential learning for neural networks, and fully-online reinforcement learning
Read more: gerdm.github.io/posts/filter...
I will cover filtering and applications—from classical tracking problems to time-series forecasting, sequential learning for neural networks, and fully-online reinforcement learning
Read more: gerdm.github.io/posts/filter...
Filtering notes (I): signal plus noise models
Introduction From tracking epidemics and predicting financial markets to controlling complex systems like rockets and personalising online experiences, many real-world problems rely on understanding a...
gerdm.github.io
January 6, 2025 at 3:11 PM
Part I of a Bayesian’s take on (Kalman) filtering—without Bayes.
I will cover filtering and applications—from classical tracking problems to time-series forecasting, sequential learning for neural networks, and fully-online reinforcement learning
Read more: gerdm.github.io/posts/filter...
I will cover filtering and applications—from classical tracking problems to time-series forecasting, sequential learning for neural networks, and fully-online reinforcement learning
Read more: gerdm.github.io/posts/filter...