# LikelihoodExample

#### class LikelihoodExample[A <: Iterable[variable.Var], B <: model.Model] extends Example

Base example for maximizing log likelihood.

Works by calling inference and getting a summary, and using the information about the factor marginals in the summary to compute the value and gradient.

It also works with MAP inference.

A

The type of the labels

B

The type of the model

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### Instance Constructors

1. #### new LikelihoodExample(labels: A, model: B, infer: Infer[A, B])

labels

The first argument to inference

model

The second argument to inference

infer

The inference routine

### Value Members

1. #### final def !=(arg0: AnyRef): Boolean

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3. #### final def ##(): Int

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Put objective value and gradient into the accumulators.

Put objective value and gradient into the accumulators. Either argument can be null if they don't need to be computed.

value

Accumulator to hold value

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7. #### final def asInstanceOf[T0]: T0

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8. #### def clone(): AnyRef

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11. #### def finalize(): Unit

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14. #### val infer: Infer[A, B]

The inference routine

15. #### final def isInstanceOf[T0]: Boolean

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16. #### final def ne(arg0: AnyRef): Boolean

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17. #### final def notify(): Unit

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