Download BayesiaLab v3.3 keygen by DIGERATI

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  ▀███▓███▓▀▀                The diGERATi Present                ▀▀▓███▓███▀    
      ▀▀▀                                                                      
  █▄▄▄▄▄                       BayesiaLab v3.3                      ▄▄▄▄▄▄█
    ▀███▓▄                                                           ▄███▓▀    
     ▐██▓▓                                                           ███▓▌     
     ███▀                        Release Info                         ▀██▓     
   ▄██▀                                                                 ▀██▄   
 ▄██▀                                                                     ▀██▄ 
▐█▓▓      Cracker ......: DIGERATI       Release-Type..: Regged             ██▓▌
██▓▌      Packager......: DIGPACKER      Release-Date..: 5-6-2005           ▐██▓
██▓       Supplier......: DIGERATI       Protection....: Serial             ██▓
▐██       Coder.........: DIGERATI       OS............: WinAll             █▓▌
 ██▓ ░    Company.......: Bayesia        Disks.........: [xx/03]           ░▓█▓ 
  ▀██░                                                                   ░█▓▀  
  ▀██░    URL: http://www.bayesia.com/                                   ░█▓▀  
▓▄ ▐█▓▌                                                                 ▐█▓▌ ▄▓
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█▄    █▄▀▀████▓▀   ▐█▓▌    ▀     Release Notes     ▀    ▐█▓▌   ▀▓████▀▀▄█    ▄█
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▐█▓▌                                                                       ▐█▓▌
██▓                                                                         ██▓
█▓█         With BayesiaLab, Bayesia gives you a complete                   ██▓
█▓█         laboratory for manipulating Bayesian networks:                  ██▓
█▓█                                                                         ██▓
█▓█         * Develop your decision models through                          ██▓
█▓█         expertise                                                       ██▓
█▓█         o Ergonomic node edition                                        ██▓
█▓█         panel providing:                                                ██▓
█▓█         + wizards for                                                   ██▓
█▓█         modality generation and naming of both Label                    ██▓
█▓█         and Interval nodes                                              ██▓
█▓█         + various entry                                                 ██▓
█▓█         modes for conditional probability distribution:                 ██▓
█▓█         probabilistic, deterministic and equation                       ██▓
█▓█         + powerful formula                                              ██▓
█▓█         editor with a complete function and operator                    ██▓
█▓█         library (discrete & continuous probability                      ██▓
█▓█         distributions, arithmetic and trigonometric                     ██▓
█▓█         functions, ...)                                                 ██▓
█▓█         + table completion                                              ██▓
█▓█         and normalization tools, cut & paste between                    ██▓
█▓█         tables and external applications                                ██▓
█▓█         o Constraint nodes to                                           ██▓
█▓█         express constraint that hold between nodes                      ██▓
█▓█         o Enhanced traceability and                                     ██▓
█▓█         documentation thanks to hypertext comments                      ██▓
█▓█         associated to the graph, the nodes and the                      ██▓
█▓█         arcs                                                            ██▓
█▓█         o Color node and arc                                            ██▓
█▓█         tagging to semantically group your variables and                ██▓
█▓█         probabilistic relations                                         ██▓
█▓█         * Automatic learning or updating of                             ██▓
█▓█         your models from your data (text files and                      ██▓
█▓█         databases)                                                      ██▓
█▓█         o Learning conditional                                          ██▓
█▓█         probabilities for a given network                               ██▓
█▓█         o Discovering of all the                                        ██▓
█▓█         probabilistic relations that hold in your data                  ██▓
█▓█         base (Association discovery)                                    ██▓
█▓█         o Supervised learning                                           ██▓
█▓█         entirely devoted to characterizing a target                     ██▓
█▓█         variable                                                        ██▓
█▓█         o Selection of the minimal                                      ██▓
█▓█         subset of variables correlated to the target                    ██▓
█▓█         variable                                                        ██▓
█▓█         o Bayesian Clustering to                                        ██▓
█▓█         invent new concepts                                             ██▓
█▓█         o Robust Missing value                                          ██▓
█▓█         processing                                                      ██▓
█▓█         o Validation tools qualifying                                   ██▓
█▓█         the obtained models (confusion matrix, lift and                 ██▓
█▓█         Roc curves)                                                     ██▓
█▓█         * Quickly assimilate the represented                            ██▓
█▓█         knowledge using a set of original analytical                    ██▓
█▓█         tools                                                           ██▓
█▓█         o Strength of the                                               ██▓
█▓█         probabilistic relations (arc's thickness and HTML               ██▓
█▓█         report)                                                         ██▓
█▓█         o Amount of information                                         ██▓
█▓█         brought to the target node/modality                             ██▓
█▓█         o Type of probabilistic                                         ██▓
█▓█         relations                                                       ██▓
█▓█         o Complete HTML analysis                                        ██▓
█▓█         report of the target variable                                   ██▓
█▓█         o HTML report of the                                            ██▓
█▓█         evidence set analysis                                           ██▓
█▓█         o Causal analysis (essential                                    ██▓
█▓█         graphs)                                                         ██▓
█▓█         o Automatic network layouting                                   ██▓
█▓█         algorithms                                                      ██▓
█▓█         * Use the models in interactive or                              ██▓
█▓█         batch mode                                                      ██▓
█▓█         o Positive, negative and                                        ██▓
█▓█         soft evidences on the variable states                           ██▓
█▓█         o Simulation of "What-if"                                       ██▓
█▓█         scenarios with probability variation highlighting               ██▓
█▓█         o Adaptive questionnaires                                       ██▓
█▓█         taking into account Costs and Information Gains                 ██▓
█▓█         o Off-line Tagging of new                                       ██▓
█▓█         cases contained in a file                                       ██▓
█▓█         o Robust imputation algorithm                                   ██▓
█▓█         to complete data with missing values                            ██▓
█▓█         * Introduce the temporal dimension into                         ██▓
█▓█         your models                                                     ██▓
█▓█         o Compact representation of                                     ██▓
█▓█         Dynamic Bayesian networks                                       ██▓
█▓█         o Time node for an explicit                                     ██▓
█▓█         use of time in the equations                                    ██▓
█▓█         o Temporal simulation step                                      ██▓
█▓█         by step or by period with a graphical view of                   ██▓
█▓█         the probability evolution                                       ██▓
█▓█         o Observations file to                                          ██▓
█▓█         specify the context of the scenarios                            ██▓
█▓█         * Representation, evaluation and                                ██▓
█▓█         learning of your action policies                                ██▓
█▓█         o Decision nodes for                                            ██▓
█▓█         modeling your actions                                           ██▓
█▓█         o Quality tables associated                                     ██▓
█▓█         to Decision nodes for a direct representation                   ██▓
█▓█         of action policies                                              ██▓
█▓█         o Utility nodes to valuate                                      ██▓
█▓█         the states and to associate cost/gains to node                  ██▓
█▓█         modalities                                                      ██▓
█▓█         o Reinforcement learning                                        ██▓
█▓█         algorithms to automatically discover policies                   ██▓
█▓█         that optimize the expected sum of utilities                     ██▓
█▓█         * Complete interoperability                                     ██▓
█▓█         o Connection with your                                          ██▓
█▓█         databases by using JDBC/ODBC                                    ██▓
█▓█         o SQL Interface                                                 ██▓
█▓█         o Exportation of Bayesian                                       ██▓
█▓█         networks, tables, equations, graphs, matrices and               ██▓
█▓█         reports by using simple copy&paste, as images,                  ██▓
█▓█         numerical data and HTML texts                                   ██▓
██▓                                                                         ██▓
██▓                                                                         ██▓
██▓                                                                         ██▓
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▐█▓▌                                                                       ▐█▓▌
 ██▓ ░ ▄ ▀▀▄                                                       ▄▀▀ ▄ ░ ▓█▓ 
  ▀██▓▄▄   ▐██▄        ▄        Install  Notes         ▄        ▄█▓▌   ▄▄▓██▀  
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██▓ ▀ ▐█▌                                                             ▐█▌ ▀ █▓▓
▀██▓▄▄█▓                                                               █▓▄▄██▓▀
▄ ▀▀▀▀▀                                                                 ▀▀▀▀▀ ▄
█▓  █▓▀                                                                 ▀▓█  █▓
█▓█         1. Unpack the pre-cracked software                              ██▓
█▓█         2. Use the following serials to register                        ██▓
█▓█                                                                         ██▓
█▓█         10A3-9A30-6473-04B8                                             ██▓
█▓█                                                                         ██▓
█▓█         45B3-3JKU-5283-FSGU                                             ██▓
█▓▌▀█                                                                     █▀▐█▓
██▓▌                                                                       ▐██▓
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▀███▓▓▀  ▀▀                                                         ▀▀ ▀▀██▓▓▓▀
 ▄  ▄▄▄█          Greets to all who work hard to make the scene       █▄▄▄  ▄ 
▄█▓  █▓▌                                                              ▐█▓  █▓▄
█▓▌  ▐█▓              	secure and enjoyable for everyone! 	       █▓▌  ▐█▓
▀█▓   █▓▌   						     	      ▐█▓   █▓▀
  ▀▀▄ █▓▌         Some general notes...                              ▐█▓ ▄▀▀  
     ▐█▓                                                               █▓▌     
     ██▓ █     - we dont want our releases spread via usenet,ftp    █ ██▓     
    ▐█▓  ▐█    - we dont want our releases being listed on 	   ▓▌  █▓▌    
    █▓    █▓     several public dupe-sites like nforce		   ██▓    █▓    
   █▓    █▓▓   - we dont want to see our releases on public places  ██▓    █▓   
 ▄█▓   ▄█▓▓    - nor do we support one of these points               ██▓▄   █▓▄ 
▐█▓▌  ██▓▓                                                           ██▓▓  ▐█▓▌
▐██▓ ██▓▀      Think about it and try to make the scene as secure... ▀██▓ ██▓▓
 ▀███▄▄▄░                    ... as possible                       ░▄▄▄█▓▓▀ 
  ▄▄▀▀▀█▓█▄▄▄ ░                                                 ░ ▄▄▄█▓█▀▀▀▄▄  
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 █▓▓▀         ░                                                 ░         ▀▓▓█ 
▐▓▌ Ascii by Yce*Remorse                                                    ▐▓▌
 ▀█▄▄                                                                     ▄█▀ 
     ▀▀▀▄▄                                                           ▄▄▀▀▀     
            ▀                                                     ▀


FILE_ID.DIZ

DIGERATI presents keygen for BayesiaLab v3.3


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