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种子名称： pgm
文件类型：
文件数目： 94个文件
文件大小： 1.36 GB
收录时间： 2012-5-19 17:21
已经下载： 3次
资源热度： 86
最近下载： 2025-1-21 23:47

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pgm.torrent

19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp434.61MB

23 - 1 - Class Summary (24-38).mp432.21MB

15 - 1 - Maximum Expected Utility (25-57).mp428.99MB

20 - 6 - Learning General Graphs- Heuristic Search (23-36).mp426.77MB

21 - 5 - Latent Variables (22-00).mp426.7MB

3 - 2 - Temporal Models - DBNs (23-02).mp426.07MB

6 - 6 - Log-Linear Models (22-08).mp425.77MB

22 - 1 - Summary- Learning (20-11).mp425.69MB

6 - 3 - Conditional Random Fields (22-22).mp425.06MB

21 - 1 - Learning With Incomplete Data - Overview (21-34).mp424.86MB

7 - 1 - Knowledge Engineering (23-05).mp424.65MB

1 - 2 - Overview and Motivation (19-17).mp423MB

20 - 4 - Bayesian Scores (20-35).mp422.62MB

3 - 4 - Plate Models (20-08).mp422.48MB

6 - 5 - I-maps and perfect maps (20-59).mp422.41MB

2 - 5 - Independencies in Bayesian Networks (18-18).mp421.54MB

18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).mp421.16MB

4 - 2 - Moving Data Around (16-07).mp420.77MB

15 - 2 - Utility Functions (18-15).mp419.68MB

2 - 1 - Semantics & Factorization (17-20).mp419.56MB

15 - 3 - Value of Perfect Information (17-14).mp419.28MB

6 - 2 - General Gibbs Distribution (15-52).mp418.93MB

20 - 2 - Likelihood Scores (16-49).mp418.73MB

18 - 3 - Bayesian Estimation (15-27).mp418.66MB

21 - 2 - Expectation Maximization - Intro (16-17).mp418.07MB

18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).mp417.72MB

4 - 1 - Basic Operations (13-59).mp417.71MB

20 - 7 - Learning General Graphs- Search and Decomposability (15-46).mp417.64MB

17 - 1 - Learning- Overview (15-35).mp417.51MB

13 - 5 - Metropolis Hastings Algorithm (27-06).mp416.91MB

4 - 5 - Control Statements- for, while, if statements (12-55).mp416.49MB

18 - 4 - Bayesian Prediction (13-40).mp416.21MB

4 - 6 - Vectorization (13-48).mp416.09MB

5 - 2 - Tree-Structured CPDs (14-37).mp416.04MB

5 - 3 - Independence of Causal Influence (13-08).mp415.87MB

2 - 4 - Conditional Independence (12-38).mp415.52MB

2 - 3 - Flow of Probabilistic Influence (14-36).mp415.47MB

5 - 4 - Continuous Variables (13-25).mp415.34MB

4 - 3 - Computing On Data (13-15).mp415.25MB

18 - 1 - Maximum Likelihood Estimation (14-59).mp415.15MB

19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).mp415.1MB

20 - 5 - Learning Tree Structured Networks (12-05).mp414.46MB

16 - 4 - Model Selection and Train Validation Test Sets (12-03).mp414.07MB

13 - 1 - Simple Sampling (23-37).mp413.78MB

3 - 3 - Temporal Models - HMMs (12-01).mp413.58MB

14 - 1 - Inference in Temporal Models (19-43).mp413.56MB

4 - 4 - Plotting Data (09-38).mp413.32MB

9 - 1 - Belief Propagation (21-21).mp413.25MB

10 - 7 - Loopy BP and Message Decoding (21-42).mp413.15MB

21 - 3 - Analysis of EM Algorithm (11-32).mp412.88MB

2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).mp412.76MB

21 - 4 - EM in Practice (11-17).mp412.69MB

11 - 1 - Max Sum Message Passing (20-27).mp412.65MB

16 - 6 - Regularization and Bias Variance (11-20).mp412.6MB

6 - 1 - Pairwise Markov Networks (10-59).mp412.56MB

20 - 3 - BIC and Asymptotic Consistency (11-26).mp412.53MB

13 - 4 - Gibbs Sampling (19-26).mp412.5MB

16 - 2 - Regularization- Cost Function (10-10).mp411.63MB

3 - 1 - Overview of Template Models (10-55).mp411.57MB

2 - 7 - Application - Medical Diagnosis (09-19).mp411.51MB

19 - 3 - MAP Estimation for MRFs and CRFs (9-59).mp411.29MB

12 - 2 - Dual Decomposition - Intuition (17-46).mp411.2MB

16 - 1 - Regularization- The Problem of Overfitting (09-42).mp411.15MB

8 - 3 - Variable Elimination Algorithm (16-17).mp411.11MB

2 - 2 - Reasoning Patterns (09-59).mp410.78MB

2 - 6 - Naive Bayes (09-52).mp410.63MB

10 - 5 - Clique Trees and VE (16-17).mp410.55MB

10 - 2 - Clique Tree Algorithm - Correctness (18-23).mp410.48MB

6 - 7 - Shared Features in Log-Linear Models (08-28).mp410.02MB

12 - 3 - Dual Decomposition - Algorithm (16-16).mp49.74MB

9 - 2 - Properties of Cluster Graphs (15-00).mp49.73MB

12 - 1 - Tractable MAP Problems (15-04).mp49.69MB

5 - 1 - Overview- Structured CPDs (08-00).mp49.65MB

8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).mp49.55MB

13 - 3 - Using a Markov Chain (15-27).mp49.53MB

10 - 4 - Clique Trees and Independence (15-21).mp49.52MB

13 - 2 - Markov Chain Monte Carlo (14-18).mp49.21MB

10 - 6 - BP In Practice (15-38).mp49.2MB

8 - 1 - Overview- Conditional Probability Queries (15-22).mp49.01MB

16 - 5 - Diagnosing Bias vs Variance (07-42).mp48.97MB

8 - 6 - Finding Elimination Orderings (11-58).mp48.77MB

10 - 3 - Clique Tree Algorithm - Computation (16-18).mp48.72MB

8 - 4 - Complexity of Variable Elimination (12-48).mp48.58MB

16 - 3 - Evaluating a Hypothesis (07-35).mp48.48MB

14 - 2 - Inference- Summary (12-45).mp47.83MB

1 - 4 - Factors (06-40).mp47.37MB

1 - 1 - Welcome! (05-35).mp47.11MB

20 - 1 - Structure Learning Overview (5-49).mp46.66MB

8 - 2 - Overview- MAP Inference (09-42).mp45.87MB

6 - 4 - Independencies in Markov Networks (04-48).mp45.84MB

1 - 3 - Distributions (04-56).mp45.81MB

10 - 1 - Properties of Belief Propagation (9-31).mp45.75MB

4 - 7 - Working on and Submitting Programming Exercises (03-33).mp45.5MB

11 - 2 - Finding a MAP Assignment (3-57).mp42.67MB