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pgm

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
2012-5-19 17:21 2024-1-20 04:53 76 1.36 GB 94
磁力链接
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迅雷链接
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pgm的二维码
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pgm

文件列表
  1. 19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp434.61MB
  2. 23 - 1 - Class Summary (24-38).mp432.21MB
  3. 15 - 1 - Maximum Expected Utility (25-57).mp428.99MB
  4. 20 - 6 - Learning General Graphs- Heuristic Search (23-36).mp426.77MB
  5. 21 - 5 - Latent Variables (22-00).mp426.7MB
  6. 3 - 2 - Temporal Models - DBNs (23-02).mp426.07MB
  7. 6 - 6 - Log-Linear Models (22-08).mp425.77MB
  8. 22 - 1 - Summary- Learning (20-11).mp425.69MB
  9. 6 - 3 - Conditional Random Fields (22-22).mp425.06MB
  10. 21 - 1 - Learning With Incomplete Data - Overview (21-34).mp424.86MB
  11. 7 - 1 - Knowledge Engineering (23-05).mp424.65MB
  12. 1 - 2 - Overview and Motivation (19-17).mp423MB
  13. 20 - 4 - Bayesian Scores (20-35).mp422.62MB
  14. 3 - 4 - Plate Models (20-08).mp422.48MB
  15. 6 - 5 - I-maps and perfect maps (20-59).mp422.41MB
  16. 2 - 5 - Independencies in Bayesian Networks (18-18).mp421.54MB
  17. 18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).mp421.16MB
  18. 4 - 2 - Moving Data Around (16-07).mp420.77MB
  19. 15 - 2 - Utility Functions (18-15).mp419.68MB
  20. 2 - 1 - Semantics & Factorization (17-20).mp419.56MB
  21. 15 - 3 - Value of Perfect Information (17-14).mp419.28MB
  22. 6 - 2 - General Gibbs Distribution (15-52).mp418.93MB
  23. 20 - 2 - Likelihood Scores (16-49).mp418.73MB
  24. 18 - 3 - Bayesian Estimation (15-27).mp418.66MB
  25. 21 - 2 - Expectation Maximization - Intro (16-17).mp418.07MB
  26. 18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).mp417.72MB
  27. 4 - 1 - Basic Operations (13-59).mp417.71MB
  28. 20 - 7 - Learning General Graphs- Search and Decomposability (15-46).mp417.64MB
  29. 17 - 1 - Learning- Overview (15-35).mp417.51MB
  30. 13 - 5 - Metropolis Hastings Algorithm (27-06).mp416.91MB
  31. 4 - 5 - Control Statements- for, while, if statements (12-55).mp416.49MB
  32. 18 - 4 - Bayesian Prediction (13-40).mp416.21MB
  33. 4 - 6 - Vectorization (13-48).mp416.09MB
  34. 5 - 2 - Tree-Structured CPDs (14-37).mp416.04MB
  35. 5 - 3 - Independence of Causal Influence (13-08).mp415.87MB
  36. 2 - 4 - Conditional Independence (12-38).mp415.52MB
  37. 2 - 3 - Flow of Probabilistic Influence (14-36).mp415.47MB
  38. 5 - 4 - Continuous Variables (13-25).mp415.34MB
  39. 4 - 3 - Computing On Data (13-15).mp415.25MB
  40. 18 - 1 - Maximum Likelihood Estimation (14-59).mp415.15MB
  41. 19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).mp415.1MB
  42. 20 - 5 - Learning Tree Structured Networks (12-05).mp414.46MB
  43. 16 - 4 - Model Selection and Train Validation Test Sets (12-03).mp414.07MB
  44. 13 - 1 - Simple Sampling (23-37).mp413.78MB
  45. 3 - 3 - Temporal Models - HMMs (12-01).mp413.58MB
  46. 14 - 1 - Inference in Temporal Models (19-43).mp413.56MB
  47. 4 - 4 - Plotting Data (09-38).mp413.32MB
  48. 9 - 1 - Belief Propagation (21-21).mp413.25MB
  49. 10 - 7 - Loopy BP and Message Decoding (21-42).mp413.15MB
  50. 21 - 3 - Analysis of EM Algorithm (11-32).mp412.88MB
  51. 2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).mp412.76MB
  52. 21 - 4 - EM in Practice (11-17).mp412.69MB
  53. 11 - 1 - Max Sum Message Passing (20-27).mp412.65MB
  54. 16 - 6 - Regularization and Bias Variance (11-20).mp412.6MB
  55. 6 - 1 - Pairwise Markov Networks (10-59).mp412.56MB
  56. 20 - 3 - BIC and Asymptotic Consistency (11-26).mp412.53MB
  57. 13 - 4 - Gibbs Sampling (19-26).mp412.5MB
  58. 16 - 2 - Regularization- Cost Function (10-10).mp411.63MB
  59. 3 - 1 - Overview of Template Models (10-55).mp411.57MB
  60. 2 - 7 - Application - Medical Diagnosis (09-19).mp411.51MB
  61. 19 - 3 - MAP Estimation for MRFs and CRFs (9-59).mp411.29MB
  62. 12 - 2 - Dual Decomposition - Intuition (17-46).mp411.2MB
  63. 16 - 1 - Regularization- The Problem of Overfitting (09-42).mp411.15MB
  64. 8 - 3 - Variable Elimination Algorithm (16-17).mp411.11MB
  65. 2 - 2 - Reasoning Patterns (09-59).mp410.78MB
  66. 2 - 6 - Naive Bayes (09-52).mp410.63MB
  67. 10 - 5 - Clique Trees and VE (16-17).mp410.55MB
  68. 10 - 2 - Clique Tree Algorithm - Correctness (18-23).mp410.48MB
  69. 6 - 7 - Shared Features in Log-Linear Models (08-28).mp410.02MB
  70. 12 - 3 - Dual Decomposition - Algorithm (16-16).mp49.74MB
  71. 9 - 2 - Properties of Cluster Graphs (15-00).mp49.73MB
  72. 12 - 1 - Tractable MAP Problems (15-04).mp49.69MB
  73. 5 - 1 - Overview- Structured CPDs (08-00).mp49.65MB
  74. 8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).mp49.55MB
  75. 13 - 3 - Using a Markov Chain (15-27).mp49.53MB
  76. 10 - 4 - Clique Trees and Independence (15-21).mp49.52MB
  77. 13 - 2 - Markov Chain Monte Carlo (14-18).mp49.21MB
  78. 10 - 6 - BP In Practice (15-38).mp49.2MB
  79. 8 - 1 - Overview- Conditional Probability Queries (15-22).mp49.01MB
  80. 16 - 5 - Diagnosing Bias vs Variance (07-42).mp48.97MB
  81. 8 - 6 - Finding Elimination Orderings (11-58).mp48.77MB
  82. 10 - 3 - Clique Tree Algorithm - Computation (16-18).mp48.72MB
  83. 8 - 4 - Complexity of Variable Elimination (12-48).mp48.58MB
  84. 16 - 3 - Evaluating a Hypothesis (07-35).mp48.48MB
  85. 14 - 2 - Inference- Summary (12-45).mp47.83MB
  86. 1 - 4 - Factors (06-40).mp47.37MB
  87. 1 - 1 - Welcome! (05-35).mp47.11MB
  88. 20 - 1 - Structure Learning Overview (5-49).mp46.66MB
  89. 8 - 2 - Overview- MAP Inference (09-42).mp45.87MB
  90. 6 - 4 - Independencies in Markov Networks (04-48).mp45.84MB
  91. 1 - 3 - Distributions (04-56).mp45.81MB
  92. 10 - 1 - Properties of Belief Propagation (9-31).mp45.75MB
  93. 4 - 7 - Working on and Submitting Programming Exercises (03-33).mp45.5MB
  94. 11 - 2 - Finding a MAP Assignment (3-57).mp42.67MB
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