spm/spm
Apr 2023 – Jan 2026
26 Months active
Languages Used
MATLAB
Technical Skills
Bayesian inferenceMarkov decision processesactive inferencealgorithm developmentdata analysismachine learning
Karl Friston
PROFILE
Karl Friston
Over 26 months, Karl Friston developed advanced generative modeling, structure learning, and simulation features for the spm/spm repository, focusing on scalable active inference and decision-support workflows. He engineered recursive Markov Decision Processes, high-dimensional data encoding, and robust reinforcement learning for domains such as Atari games, COVID-19 modeling, and smart energy systems. Using MATLAB and Bayesian inference, Karl integrated probabilistic modeling, data compression, and simulation pipelines, enhancing model accuracy, maintainability, and deployment readiness. His work demonstrated depth in algorithm development and statistical modeling, delivering end-to-end solutions that improved experimentation speed, reproducibility, and the reliability of analytics across diverse applications.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
70Total
Bugs
5
Commits
70
Features
45
Lines of code
62,795
Activity Months26
Your Network
62 peopleShared Repositories
22
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for spm/spm: Implemented NESS Forecasting and Parameterization Enhancements with predictive density estimation and refined algorithms, boosting accuracy and robustness of Nonequilibrium Steady-State models. This work enables quantified uncertainty, better parameterization, and more reliable dynamic-system forecasts, supporting improved decision-making and risk assessment.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for spm/spm: Implemented NESS Forecasting and Parameterization Enhancements with predictive density estimation and refined algorithms, boosting accuracy and robustness of Nonequilibrium Steady-State models. This work enables quantified uncertainty, better parameterization, and more reliable dynamic-system forecasts, supporting improved decision-making and risk assessment.
January 2026
December 2025
2 Commits • 1 Features
Dec 1, 2025Month 2025-12 - spm/spm: Focused on expanding predictive modeling capabilities and spatial interpretation through two key deliverables. Feature set delivered includes: (1) Advanced Modeling Enhancements: NESS Generative Models for flow dynamics and forecasting future states using polynomial expansions of model parameters; (2) Bayesian Model Reduction: converts discrete likelihood measures into a continuous posterior distribution, with visualization and estimation of location and dispersion to support nuanced spatial interpretations. No major bugs reported this month. Overall impact includes improved forecasting accuracy, richer inference for discrete-to-continuous state transitions, and stronger business value for downstream analytics and spatial decision-making. Technologies and skills demonstrated include generative modeling, Bayesian inference, polynomial dynamics, data visualization, and robust version-control practices (SVN integration and commit tracking).
December 2025
2 Commits • 1 Features
Dec 1, 2025Month 2025-12 - spm/spm: Focused on expanding predictive modeling capabilities and spatial interpretation through two key deliverables. Feature set delivered includes: (1) Advanced Modeling Enhancements: NESS Generative Models for flow dynamics and forecasting future states using polynomial expansions of model parameters; (2) Bayesian Model Reduction: converts discrete likelihood measures into a continuous posterior distribution, with visualization and estimation of location and dispersion to support nuanced spatial interpretations. No major bugs reported this month. Overall impact includes improved forecasting accuracy, richer inference for discrete-to-continuous state transitions, and stronger business value for downstream analytics and spatial decision-making. Technologies and skills demonstrated include generative modeling, Bayesian inference, polynomial dynamics, data visualization, and robust version-control practices (SVN integration and commit tracking).
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for spm/spm focused on feature delivery and business impact. Highlighted capability upgrade in financial modeling through new reasoning rules, and associated improvements to data structures and processing efficiency.
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for spm/spm focused on feature delivery and business impact. Highlighted capability upgrade in financial modeling through new reasoning rules, and associated improvements to data structures and processing efficiency.
November 2025
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for spm/spm focused on delivering capabilities to support data-driven model selection and clearer result interpretation. No major bugs fixed this month. Key features delivered include Active Reasoning in MDP schemes and enhanced plotting for Bayesian model comparison and DCM results, enabling more informed decision-making and stakeholder communication. Impact includes improved information-gain based model evaluation, better visualization of model comparison outcomes, and stronger traceability through explicit commit references. Technologies/skills demonstrated include Bayesian inference, active learning concepts, MDP schemes, data visualization enhancements, and rigorous version control practices.
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for spm/spm focused on delivering capabilities to support data-driven model selection and clearer result interpretation. No major bugs fixed this month. Key features delivered include Active Reasoning in MDP schemes and enhanced plotting for Bayesian model comparison and DCM results, enabling more informed decision-making and stakeholder communication. Impact includes improved information-gain based model evaluation, better visualization of model comparison outcomes, and stronger traceability through explicit commit references. Technologies/skills demonstrated include Bayesian inference, active learning concepts, MDP schemes, data visualization enhancements, and rigorous version control practices.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly performance summary for spm/spm focused on distributed agent modeling and API/documentation clarity. Key features delivered include a Swarm Behavior Demo and DEM Toolbox Expansion, enabling autonomous agents, self-organization, and inter-agent interactions. The feature updates encompassed a pattern-formation based swarm demo and updates to spm_ADEM to support new action structures, expanding the DEM toolbox functionality. This work provides a tangible demonstration of distributed dynamics and lays groundwork for future optimization and control use cases. Major bugs fixed include a documentation correction for the DEM_masks_Cam usage in Bayesian model reduction for COVID models, ensuring the correct input format and reducing potential usage errors. The documentation update improves onboarding, reproducibility, and model reliability. Overall impact includes strengthened modeling capabilities for distributed systems, improved API clarity and maintainability, and better traceability of changes via versioned commits. Technologies demonstrated include the DEM toolbox, swarm dynamics demos, action-structure integration in spm_ADEM, and rigorous documentation practices. SVN references: r8503 (commit 130f0ecac3a21ae7e756c386794b7d01de2b03c1) and r8504 (commit dd5f003916ce260c912e616d4f6fb7d684cb13b9).
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly performance summary for spm/spm focused on distributed agent modeling and API/documentation clarity. Key features delivered include a Swarm Behavior Demo and DEM Toolbox Expansion, enabling autonomous agents, self-organization, and inter-agent interactions. The feature updates encompassed a pattern-formation based swarm demo and updates to spm_ADEM to support new action structures, expanding the DEM toolbox functionality. This work provides a tangible demonstration of distributed dynamics and lays groundwork for future optimization and control use cases. Major bugs fixed include a documentation correction for the DEM_masks_Cam usage in Bayesian model reduction for COVID models, ensuring the correct input format and reducing potential usage errors. The documentation update improves onboarding, reproducibility, and model reliability. Overall impact includes strengthened modeling capabilities for distributed systems, improved API clarity and maintainability, and better traceability of changes via versioned commits. Technologies demonstrated include the DEM toolbox, swarm dynamics demos, action-structure integration in spm_ADEM, and rigorous documentation practices. SVN references: r8503 (commit 130f0ecac3a21ae7e756c386794b7d01de2b03c1) and r8504 (commit dd5f003916ce260c912e616d4f6fb7d684cb13b9).
June 2025
April 2025
7 Commits • 4 Features
Apr 1, 2025April 2025 (spm/spm): Delivered cross-domain feature enhancements spanning arcade game demo, de novo structure learning, diabetes dynamic modeling (DCM) with enhanced DEM UI, and COVID-19 modeling. Focused on performance, usability, and demonstrable domain capabilities. No explicit bug fixes reported this month; prioritized feature-rich updates with clear traceability to SVN revisions r8496–r8502. These changes collectively improve simulation fidelity, user experience, and readiness for stakeholder demonstrations.
April 2025
7 Commits • 4 Features
Apr 1, 2025April 2025 (spm/spm): Delivered cross-domain feature enhancements spanning arcade game demo, de novo structure learning, diabetes dynamic modeling (DCM) with enhanced DEM UI, and COVID-19 modeling. Focused on performance, usability, and demonstrable domain capabilities. No explicit bug fixes reported this month; prioritized feature-rich updates with clear traceability to SVN revisions r8496–r8502. These changes collectively improve simulation fidelity, user experience, and readiness for stakeholder demonstrations.
March 2025
3 Commits • 3 Features
Mar 1, 2025March 2025: Delivered three focused feature enhancements in spm/spm, advancing learning efficiency, decision-making demonstrations under active inference, and demo quality/performance. No major bugs fixed in this period; refactors and enhancements set the stage for faster experimentation, clearer results, and more robust demos.
3 Commits • 3 Features
Mar 1, 2025March 2025: Delivered three focused feature enhancements in spm/spm, advancing learning efficiency, decision-making demonstrations under active inference, and demo quality/performance. No major bugs fixed in this period; refactors and enhancements set the stage for faster experimentation, clearer results, and more robust demos.
March 2025
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025: Consolidated delivery in spm/spm including a new learning model for Atari games with pullback attractors and NESS dynamics, plus an active inference-driven inspection drone demo for autonomous exploration and cleaning. Simultaneously improved reliability and correctness by fixing critical control and constraint issues (HVAC and MDP), demonstrating end-to-end value from advanced learning models to robust system behavior.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025: Consolidated delivery in spm/spm including a new learning model for Atari games with pullback attractors and NESS dynamics, plus an active inference-driven inspection drone demo for autonomous exploration and cleaning. Simultaneously improved reliability and correctness by fixing critical control and constraint issues (HVAC and MDP), demonstrating end-to-end value from advanced learning models to robust system behavior.
January 2025
2 Commits • 2 Features
Jan 1, 2025January 2025: Delivered two primary features in spm/spm, focusing on efficient structure learning for Atari simulations and an active-inference-based smart thermostat demo for energy management. These efforts advance scalable learning dynamics in simulations and demonstrate hierarchical decision-making for cost-aware home energy optimization, supporting both research progress and potential product viability.
2 Commits • 2 Features
Jan 1, 2025January 2025: Delivered two primary features in spm/spm, focusing on efficient structure learning for Atari simulations and an active-inference-based smart thermostat demo for energy management. These efforts advance scalable learning dynamics in simulations and demonstrate hierarchical decision-making for cost-aware home energy optimization, supporting both research progress and potential product viability.
January 2025
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for spm/spm focusing on business value and technical achievements. Delivered feature enhancements for the Atari demos generative model and completed codebase cleanup of obsolete Atari learning and WAV image processing utilities. These efforts reduce technical debt, improve maintainability, and prepare the project for upcoming work. No major bugs fixed this month; emphasis was on features and code hygiene.
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for spm/spm focusing on business value and technical achievements. Delivered feature enhancements for the Atari demos generative model and completed codebase cleanup of obsolete Atari learning and WAV image processing utilities. These efforts reduce technical debt, improve maintainability, and prepare the project for upcoming work. No major bugs fixed this month; emphasis was on features and code hygiene.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 — Spm/spm: Enhanced RGM structure learning with streams in hierarchical models and a new function for structure learning from pixels, enabling learning from sparse rewards and better data integration across conditions. Implemented performance optimizations to accelerate structure learning and addressed stability issues via a targeted bug fix. These changes extend capability for multi-condition data, accelerate experimentation, and improve data fusion from visual inputs, delivering clear business value in model reliability and experimentation cycles.
2 Commits • 1 Features
Nov 1, 2024November 2024 — Spm/spm: Enhanced RGM structure learning with streams in hierarchical models and a new function for structure learning from pixels, enabling learning from sparse rewards and better data integration across conditions. Implemented performance optimizations to accelerate structure learning and addressed stability issues via a targeted bug fix. These changes extend capability for multi-condition data, accelerate experimentation, and improve data fusion from visual inputs, delivering clear business value in model reliability and experimentation cycles.
November 2024
October 2024
3 Commits • 2 Features
Oct 1, 20242024-10 Monthly Summary for spm/spm: Delivered two major features for interactive AI simulations with clear business value and strong technical execution. ADEM Physics Demonstration in Game adds a physics-based demonstration to illustrate Active Dynamic Expectation Maximization with a generative model, including support adjustments to existing routines and an interactive demo. Daisy-chained Structure Learning for Generative Models in Atari Simulations introduces a scalable, daisy-chaining approach to integrate new training data with existing models, improving adaptability and fidelity in Atari simulations. These efforts enhance demonstration capabilities for AI research and validation, enable faster experimentation with generative models in game-like environments, and lay groundwork for scalable learning pipelines. Key outcomes include improved modularity, traceability to commits (79d4fd1eaa26f5d8e756ba4d85b72ef30d7b5605, c10c486549babbe50dc37ae666b2032183c69b0f, 119a9a1368e56102d9fe6cbc06d7002dca2cabf6). No major bugs fixed this month; primary focus on feature delivery and stabilization for the next cycle.
October 2024
3 Commits • 2 Features
Oct 1, 20242024-10 Monthly Summary for spm/spm: Delivered two major features for interactive AI simulations with clear business value and strong technical execution. ADEM Physics Demonstration in Game adds a physics-based demonstration to illustrate Active Dynamic Expectation Maximization with a generative model, including support adjustments to existing routines and an interactive demo. Daisy-chained Structure Learning for Generative Models in Atari Simulations introduces a scalable, daisy-chaining approach to integrate new training data with existing models, improving adaptability and fidelity in Atari simulations. These efforts enhance demonstration capabilities for AI research and validation, enable faster experimentation with generative models in game-like environments, and lay groundwork for scalable learning pipelines. Key outcomes include improved modularity, traceability to commits (79d4fd1eaa26f5d8e756ba4d85b72ef30d7b5605, c10c486549babbe50dc37ae666b2032183c69b0f, 119a9a1368e56102d9fe6cbc06d7002dca2cabf6). No major bugs fixed this month; primary focus on feature delivery and stabilization for the next cycle.
August 2024
3 Commits • 3 Features
Aug 1, 20242024-08 Monthly Summary for spm/spm — Delivered three core features aimed at scalable structure learning, robust high-dimensional modeling, and data pipeline efficiency. The work focused on performance, maintainability, and business value, with no explicit bug fixes logged in this period. Key outcomes include API modernization, faster learning loops, and improved model handling that enable quicker iterations and more reliable deployment. Technologies demonstrated span advanced structure learning, high-dimensional generative modeling, data integration, MNIST optimization, and codebase modernization.
3 Commits • 3 Features
Aug 1, 20242024-08 Monthly Summary for spm/spm — Delivered three core features aimed at scalable structure learning, robust high-dimensional modeling, and data pipeline efficiency. The work focused on performance, maintainability, and business value, with no explicit bug fixes logged in this period. Key outcomes include API modernization, faster learning loops, and improved model handling that enable quicker iterations and more reliable deployment. Technologies demonstrated span advanced structure learning, high-dimensional generative modeling, data integration, MNIST optimization, and codebase modernization.
August 2024
July 2024
1 Commits • 1 Features
Jul 1, 2024July 2024 — spm/spm: Spm_VBX Enhancements through refactor and drone simulation. Key features delivered: - Refactored Spm_VBX to support functional likelihood forms and to handle marginal likelihood as cells - Added drone simulation capabilities that utilize functional and marginal likelihood forms - Alignment with active inference workflows to enable more flexible scenario testing Major bugs fixed: - No major bugs reported this month; minor stability improvements were implemented during the refactor to improve robustness around functional forms Overall impact and accomplishments: - Enhanced model flexibility for complex scenarios, enabling more realistic simulations and faster iteration for risk assessment and decision support - Improved maintainability and extensibility of Spm_VBX to support future features with a cleaner design Technologies/skills demonstrated: - Python refactoring and modular design - Functional likelihood modeling and marginal likelihood handling - Drone simulation integration within active inference workflows - Version control and evidence of changes (commit 26d86a0be28ecbde02db0405f270fccd02cdb036)
July 2024
1 Commits • 1 Features
Jul 1, 2024July 2024 — spm/spm: Spm_VBX Enhancements through refactor and drone simulation. Key features delivered: - Refactored Spm_VBX to support functional likelihood forms and to handle marginal likelihood as cells - Added drone simulation capabilities that utilize functional and marginal likelihood forms - Alignment with active inference workflows to enable more flexible scenario testing Major bugs fixed: - No major bugs reported this month; minor stability improvements were implemented during the refactor to improve robustness around functional forms Overall impact and accomplishments: - Enhanced model flexibility for complex scenarios, enabling more realistic simulations and faster iteration for risk assessment and decision support - Improved maintainability and extensibility of Spm_VBX to support future features with a cleaner design Technologies/skills demonstrated: - Python refactoring and modular design - Functional likelihood modeling and marginal likelihood handling - Drone simulation integration within active inference workflows - Version control and evidence of changes (commit 26d86a0be28ecbde02db0405f270fccd02cdb036)
June 2024
2 Commits • 1 Features
Jun 1, 20242024-06 Monthly Summary for spm/spm: Key feature delivered: MNIST Image Compression and Classification Enhancement. This feature refactors compression functions, adjusts greedy search parameters, and enhances structure learning, training data handling, model training, and accuracy reporting to boost performance and usability. Related commits: 27af36ff0907006fd0aeb889508a17b4bc3ebac3; 02861f75bc9ae61d3c5cd46d1961f9278f06af7b. Major bugs fixed: none reported this month; minor stability issues addressed during feature work. Overall impact: end-to-end MNIST workflow improved; training/data handling more robust; accuracy reporting more reliable; faster iteration for model evaluation. Technologies/skills demonstrated: refactoring, parameter tuning of greedy search, structure learning improvements, data handling, model training, and metrics reporting; maintainability and clear commit history.
2 Commits • 1 Features
Jun 1, 20242024-06 Monthly Summary for spm/spm: Key feature delivered: MNIST Image Compression and Classification Enhancement. This feature refactors compression functions, adjusts greedy search parameters, and enhances structure learning, training data handling, model training, and accuracy reporting to boost performance and usability. Related commits: 27af36ff0907006fd0aeb889508a17b4bc3ebac3; 02861f75bc9ae61d3c5cd46d1961f9278f06af7b. Major bugs fixed: none reported this month; minor stability issues addressed during feature work. Overall impact: end-to-end MNIST workflow improved; training/data handling more robust; accuracy reporting more reliable; faster iteration for model evaluation. Technologies/skills demonstrated: refactoring, parameter tuning of greedy search, structure learning improvements, data handling, model training, and metrics reporting; maintainability and clear commit history.
June 2024
May 2024
5 Commits • 4 Features
May 1, 2024May 2024 monthly summary for spm/spm. Focused on delivering scalable, high-value generative modeling capabilities, improved audio/vision processing, and enhanced simulation utilities. Business value centers on stability, efficiency, and deployment readiness across data generation, audio analytics, compression, and drone simulation.
May 2024
5 Commits • 4 Features
May 1, 2024May 2024 monthly summary for spm/spm. Focused on delivering scalable, high-value generative modeling capabilities, improved audio/vision processing, and enhanced simulation utilities. Business value centers on stability, efficiency, and deployment readiness across data generation, audio analytics, compression, and drone simulation.
March 2024
2 Commits • 1 Features
Mar 1, 2024March 2024: Delivered a consolidated feature for High-Dimensional Data Encoding with Structure Learning and Bayesian Modeling Enhancements in spm/spm. Key work includes integrating structure learning and renormalization group routines for efficient encoding of high-dimensional data, and introducing Bayesian model averaging with a new spm_edges function to map likelihoods, replacing the previous spm_VBX model selection. Minor unit testing fixes and cleanup (removal of 'close all') to improve test reliability. This work enables improved predictive performance and data compression, supporting larger-scale analyses and faster inference. SVN revisions: r8467, r8468.
2 Commits • 1 Features
Mar 1, 2024March 2024: Delivered a consolidated feature for High-Dimensional Data Encoding with Structure Learning and Bayesian Modeling Enhancements in spm/spm. Key work includes integrating structure learning and renormalization group routines for efficient encoding of high-dimensional data, and introducing Bayesian model averaging with a new spm_edges function to map likelihoods, replacing the previous spm_VBX model selection. Minor unit testing fixes and cleanup (removal of 'close all') to improve test reliability. This work enables improved predictive performance and data compression, supporting larger-scale analyses and faster inference. SVN revisions: r8467, r8468.
March 2024
February 2024
3 Commits • 1 Features
Feb 1, 2024February 2024 monthly summary for spm/spm focusing on delivering a major feature set for Atari Demos within the Active Inference framework. Implemented structure learning enhancements for the Atari environment, integrated preferred latent states into expected free energy calculations for pixel-based learning, and refactored belief updating to boost memory efficiency and prediction accuracy. These changes improved Atari demos performance in active inference tasks and strengthened the maintainability of the SPM_VBX components.
February 2024
3 Commits • 1 Features
Feb 1, 2024February 2024 monthly summary for spm/spm focusing on delivering a major feature set for Atari Demos within the Active Inference framework. Implemented structure learning enhancements for the Atari environment, integrated preferred latent states into expected free energy calculations for pixel-based learning, and refactored belief updating to boost memory efficiency and prediction accuracy. These changes improved Atari demos performance in active inference tasks and strengthened the maintainability of the SPM_VBX components.
January 2024
3 Commits • 1 Features
Jan 1, 2024Month: 2024-01 – Focused on enhancing Atari-based pixel RL through robust structure-learning capabilities and scalable training workflows. Delivered end-to-end Atari structure-learning updates: new pixel-based learning, hierarchical modeling, and an updated training loop/parameters; refactored structure-learning routines for clearer handling of generative processes, driving more robust reinforcement learning performance. Implemented an MDP generation scheme with trust-based structure learning, and modernized code paths by replacing legacy modules with a process-based approach to improve clarity and maintainability. Overall, these changes improve model stability, sample efficiency, and experimentation speed for Atari tasks, enabling faster evaluation of new architectures and training regimes.
3 Commits • 1 Features
Jan 1, 2024Month: 2024-01 – Focused on enhancing Atari-based pixel RL through robust structure-learning capabilities and scalable training workflows. Delivered end-to-end Atari structure-learning updates: new pixel-based learning, hierarchical modeling, and an updated training loop/parameters; refactored structure-learning routines for clearer handling of generative processes, driving more robust reinforcement learning performance. Implemented an MDP generation scheme with trust-based structure learning, and modernized code paths by replacing legacy modules with a process-based approach to improve clarity and maintainability. Overall, these changes improve model stability, sample efficiency, and experimentation speed for Atari tasks, enabling faster evaluation of new architectures and training regimes.
January 2024
December 2023
1 Commits • 1 Features
Dec 1, 2023December 2023 monthly summary: Delivered state-dependent likelihood co-domains for outcome inference in spm/spm, with minor upgrades to overall functionality and performance. This change enhances the model’s ability to infer outcomes across varying states and lays groundwork for more robust decision support. No major bugs fixed this month. Overall, the work improves inference accuracy, reliability, and maintainability, contributing to better model-based decision support for customers.
December 2023
1 Commits • 1 Features
Dec 1, 2023December 2023 monthly summary: Delivered state-dependent likelihood co-domains for outcome inference in spm/spm, with minor upgrades to overall functionality and performance. This change enhances the model’s ability to infer outcomes across varying states and lays groundwork for more robust decision support. No major bugs fixed this month. Overall, the work improves inference accuracy, reliability, and maintainability, contributing to better model-based decision support for customers.
November 2023
6 Commits • 3 Features
Nov 1, 2023November 2023 monthly summary for the spm/spm repository. Delivered key features and improvements across drone demonstration, MDP/DEM inference, and SARS immunity calculations. Focused on business value by enhancing model fidelity, inference robustness, and maintainability. Notable outcomes include a drone demonstration with state-dependent likelihood domains implemented using Dirichlet priors and cleanup of obsolete DEM_drone.m; state-aware MDP enhancements with refined likelihood mappings, improved message passing, and VB estimation; and a more accurate SARS immunity model through a logarithmic adjustment. Obsolete demo artifacts cleaned to reduce technical debt and maintenance effort.
6 Commits • 3 Features
Nov 1, 2023November 2023 monthly summary for the spm/spm repository. Delivered key features and improvements across drone demonstration, MDP/DEM inference, and SARS immunity calculations. Focused on business value by enhancing model fidelity, inference robustness, and maintainability. Notable outcomes include a drone demonstration with state-dependent likelihood domains implemented using Dirichlet priors and cleanup of obsolete DEM_drone.m; state-aware MDP enhancements with refined likelihood mappings, improved message passing, and VB estimation; and a more accurate SARS immunity model through a logarithmic adjustment. Obsolete demo artifacts cleaned to reduce technical debt and maintenance effort.
November 2023
August 2023
3 Commits • 2 Features
Aug 1, 2023Month: 2023-08 — Spm/spm repository focused on delivering performance and usability improvements. Key achievements include enhancements to the MDP framework for better classification accuracy and model evidence evaluation, and the introduction of inductive inference demonstrations in the DEM toolbox with accompanying demo refinements to improve usability. No major defects reported; minor tweaks were applied to demonstrations to improve clarity. The work strengthens model evaluation, agent parameterization, and developer onboarding for the DEM toolbox.
August 2023
3 Commits • 2 Features
Aug 1, 2023Month: 2023-08 — Spm/spm repository focused on delivering performance and usability improvements. Key achievements include enhancements to the MDP framework for better classification accuracy and model evidence evaluation, and the introduction of inductive inference demonstrations in the DEM toolbox with accompanying demo refinements to improve usability. No major defects reported; minor tweaks were applied to demonstrations to improve clarity. The work strengthens model evaluation, agent parameterization, and developer onboarding for the DEM toolbox.
July 2023
3 Commits • 2 Features
Jul 1, 2023July 2023 performance summary for spm/spm focused on strengthening decision-support capabilities through enhancements to MDP-based inference and data processing pipelines. Delivered a Tower of Hanoi-inspired solving framework with final-state constraints, upgraded structure learning robustness, and modernized COVID data retrieval for age-demographic analyses while simplifying legacy metrics. These efforts improved model reliability, analytics clarity, and maintainability, aligning with business goals around accurate forecasting and epidemiological insights.
3 Commits • 2 Features
Jul 1, 2023July 2023 performance summary for spm/spm focused on strengthening decision-support capabilities through enhancements to MDP-based inference and data processing pipelines. Delivered a Tower of Hanoi-inspired solving framework with final-state constraints, upgraded structure learning robustness, and modernized COVID data retrieval for age-demographic analyses while simplifying legacy metrics. These efforts improved model reliability, analytics clarity, and maintainability, aligning with business goals around accurate forecasting and epidemiological insights.
July 2023
June 2023
2 Commits • 1 Features
Jun 1, 2023In 2023-06, the spm/spm project advanced structure learning capabilities across both generative models and Markov Decision Processes (MDPs). The work focused on disentangled representations for MNIST digit recognition and enhanced learning from probabilistic sequences to improve decision-making accuracy. These efforts establish a foundation for more robust, data-efficient models and reproducible research, with concrete commits linked to the feature work.
June 2023
2 Commits • 1 Features
Jun 1, 2023In 2023-06, the spm/spm project advanced structure learning capabilities across both generative models and Markov Decision Processes (MDPs). The work focused on disentangled representations for MNIST digit recognition and enhanced learning from probabilistic sequences to improve decision-making accuracy. These efforts establish a foundation for more robust, data-efficient models and reproducible research, with concrete commits linked to the feature work.
May 2023
1 Commits • 1 Features
May 1, 2023February monthly? No, 2023-05: Focused on expanding the MDP demo's belief sharing capabilities and probabilistic sampling to enable more robust experiments and faster iteration. Delivered a feature-rich update to the MDP Belief Sharing mechanism with improved tensor handling, updated outcome probability computations, and added gamma and Dirichlet sampling utilities. This enhances modeling accuracy, experimentation reproducibility, and decision-quality insights in the spm/spm repository.
1 Commits • 1 Features
May 1, 2023February monthly? No, 2023-05: Focused on expanding the MDP demo's belief sharing capabilities and probabilistic sampling to enable more robust experiments and faster iteration. Delivered a feature-rich update to the MDP Belief Sharing mechanism with improved tensor handling, updated outcome probability computations, and added gamma and Dirichlet sampling utilities. This enhances modeling accuracy, experimentation reproducibility, and decision-quality insights in the spm/spm repository.
May 2023
April 2023
4 Commits • 1 Features
Apr 1, 2023Month: 2023-04 — spm/spm Key features delivered: - Structure Learning and Active Inference Enhancements in SPM/MDP: Improvements to structure learning for active inference; corrections in SPM routines; enhancements in Dirichlet distribution handling and variational Bayes methods to improve learning and inference performance. Commits: 7efbd10557a34a01535abfe54b993d9c0dc001a7; 3da874325d7f65e0781971c7cfe1988fd3e738e7. Major bugs fixed: - COVID Date Handling Consistency Fix: Fixed issues with date format handling across COVID-related routines to ensure consistent and accurate date interpretation and reporting. Commit: 52d621ddd9a843ebb8473ee64efa04429af105f0. - Multi-agent Handling Bug Fix in spm_MDP_VB_XX: Fixed bugs related to handling multiple agents in the spm_MDP_VB_XX function, ensuring correct processing of concentration parameters and outcomes. Commit: cedf5a4a69367d2ad781a587cf41cb457a3cce62. Overall impact and accomplishments: - Improved learning accuracy and inference performance in active inference workflows; enhanced reliability of COVID-related data handling; and robust multi-agent processing for MDP VB models, contributing to more trustworthy analytics and reporting. - Strengthened code quality and maintainability through targeted fixes and clear commit history. Technologies/skills demonstrated: - Active Inference concepts, Dirichlet distribution handling, variational Bayes, MDP schemes, and SPM routines. - MATLAB/SPM tooling, SVN-based development workflow, and bug-fix discipline.
April 2023
4 Commits • 1 Features
Apr 1, 2023Month: 2023-04 — spm/spm Key features delivered: - Structure Learning and Active Inference Enhancements in SPM/MDP: Improvements to structure learning for active inference; corrections in SPM routines; enhancements in Dirichlet distribution handling and variational Bayes methods to improve learning and inference performance. Commits: 7efbd10557a34a01535abfe54b993d9c0dc001a7; 3da874325d7f65e0781971c7cfe1988fd3e738e7. Major bugs fixed: - COVID Date Handling Consistency Fix: Fixed issues with date format handling across COVID-related routines to ensure consistent and accurate date interpretation and reporting. Commit: 52d621ddd9a843ebb8473ee64efa04429af105f0. - Multi-agent Handling Bug Fix in spm_MDP_VB_XX: Fixed bugs related to handling multiple agents in the spm_MDP_VB_XX function, ensuring correct processing of concentration parameters and outcomes. Commit: cedf5a4a69367d2ad781a587cf41cb457a3cce62. Overall impact and accomplishments: - Improved learning accuracy and inference performance in active inference workflows; enhanced reliability of COVID-related data handling; and robust multi-agent processing for MDP VB models, contributing to more trustworthy analytics and reporting. - Strengthened code quality and maintainability through targeted fixes and clear commit history. Technologies/skills demonstrated: - Active Inference concepts, Dirichlet distribution handling, variational Bayes, MDP schemes, and SPM routines. - MATLAB/SPM tooling, SVN-based development workflow, and bug-fix discipline.
Activity
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Quality Metrics
Correctness83.2%
Maintainability81.8%
Architecture83.4%
Performance80.6%
AI Usage37.2%
Skills & Technologies
Programming Languages
MATLAB
Technical Skills
Bayesian analysisBayesian inferenceCOVID-19 data processingData ModelingGUI DevelopmentHVAC systemsMATLABMATLAB ProgrammingMATLAB programmingMarkov decision processesMatlab programmingactive inferencealgorithm designalgorithm developmentalgorithm optimization
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