{"id":33374360,"url":"https://github.com/ilke-kas/probabilistic-graphic-models","last_synced_at":"2026-05-09T20:38:17.079Z","repository":{"id":318278759,"uuid":"1070019478","full_name":"ilke-kas/probabilistic-graphic-models","owner":"ilke-kas","description":"A portfolio of advanced projects in probabilistic graphical models (PGMs), featuring hands-on notebooks for model design, inference, and real-world applications. Includes work on Bayesian networks, image denoising, and creative PGM explorations. 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This repository showcases a collection of advanced, hands-on projects exploring the theory and application of probabilistic graphical models (PGMs). Each notebook is designed to be accessible to both technical and non-technical audiences, with clear explanations, visualizations, and practical code. These projects demonstrate expertise in probabilistic modeling, inference, and real-world problem solving — skills highly valued in data science, machine learning, and AI-driven industries.\n\n---\n\n## Project Notebooks\n\n### 1. Probabilistic Graphical Models — Exploratory Notebook\n**File:** `probabilistic-graphical-models-exploratory.ipynb`\n\nA comprehensive, research-style notebook introducing the foundations of probabilistic graphical models. This notebook covers:\n- Conditional independence and d-separation\n- Model complexity and parameterization\n- Bayesian networks and factor graphs\n- Inference techniques and practical examples\n- Visual explanations and mathematical derivations\n\nIdeal for readers seeking a deep yet accessible introduction to PGMs, with a blend of theory and hands-on code.\n\n---\n\n### 2. Probabilistic Graphical Models — Image Denoising Project\n**File:** `probabilistic-graphical-models-image-denoising.ipynb`\n\nA practical project applying PGMs to the challenging task of image denoising. Highlights include:\n- Formulating image denoising as an energy minimization problem\n- Implementing factor graphs and inference algorithms\n- Experimenting with real and synthetic noisy images\n- Visualizing the denoising process and energy landscape\n\nThis notebook demonstrates how probabilistic modeling can solve real-world computer vision problems, with clear code and results.\n\n---\n\n### 3. Probabilistic Graphical Models — Inference Project\n**File:** `probabilistic-graphical-models-inference-project.ipynb`\n\nA deep dive into inference algorithms and structure learning in PGMs. Key topics:\n- Exact and approximate inference methods\n- Structure learning from data\n- Applications to real datasets\n- Step-by-step code and visualizations\n\nThis project is perfect for those interested in the mechanics of inference and learning in graphical models, with a focus on practical implementation.\n\n---\n\n### 4. PGM Explorations\n**File:** `pgm-explorations..ipynb`\n\nA curated set of advanced explorations and experiments in probabilistic graphical models. This notebook features:\n- Creative applications and novel model structures\n- Comparative studies of inference techniques\n- Insights into the strengths and limitations of PGMs\n\nGreat for readers looking to see PGMs in action beyond standard textbook examples, with a focus on innovation and critical thinking.\n\n---\n\n## Why Probabilistic Graphical Models?\nProbabilistic graphical models are a cornerstone of modern AI, enabling interpretable, flexible, and powerful reasoning under uncertainty. The skills demonstrated in these projects — from model design to inference and application — are directly relevant to roles in data science, machine learning engineering, and research.\n\nIf you have questions or would like to discuss these projects, feel free to reach out!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Filke-kas%2Fprobabilistic-graphic-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Filke-kas%2Fprobabilistic-graphic-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Filke-kas%2Fprobabilistic-graphic-models/lists"}