VTX 7:00 – 6:00

• Probabilistic Inference II
  • Assertion – Any variable in a graph is said by me to be independent of any other non-descendant, given its parents. All the causality flows through the parents.
  • A belief net or Bays net is *always* acyclic and directed.
  • Traverse the graph from the bottom up, so that no node depends on a node to its left in a list.
  • Generating the list:
  • When using the list, work from the top down in the list
  • Naive Bayesian inference
    • P(a|b)P(b) = P(a,b) = P(b|a)P(a)
    • P(a|b) = (P(b|a)P(a))/P(b) 
    • Can use Bayes to decide between models – Naive Bayesian Classification
    • Use the sum of the logs of the probabilities rather than the products because otherwise we run out of bits of precision
  • The right thing to do when you don’t know anything (just have symptoms)
• Hibernate
  • Adding config.setProperty(“hbm2ddl.auto”, “update”); to the setup, so that tables can be rebuilt on demand. Nope, that didn’t work. Maybe I can’t split configuration between the config file and programmatic variables?
  • The only way that I was able to get this to work as an argument was to have a setupTables flag indicate which config to read. That works well though.
  • Got simple collections running, which means that I should be able to get networks built. Basically modified the example from Just Hibernate that starts on page 53.
  • Next, we work on getting inheritance to work. I think this will help.
• Initial Java class network thoughts, just to try storing and retrieving items
  • BaseItem
    • guid
  •  BaseNode extends BaseItem
    • node_id
    • name
  • BaseEdge extends BaseItem
    • edge_id
    • source
    • target
    • weight
  • BaseNetwork extends BaseItem
    • network_id
    • name
    • owner
    • edgeList
    • nodeList (we need this because we may have orphans in the network)
  • BaseOwner extends BaseItem
    • owner_id
    • name
    • password?