All Implemented Interfaces:


public class SolverPosterior
extends EnsembleSolver

Posterior solver for Bayesian-style parameter uncertainty analysis.

This solver wraps another solver and handles Prior distributions by expanding the model into a family of networks, one for each alternative in the Prior. Results are aggregated using prior-weighted expectations.

Usage:

Prior prior = new Prior(Arrays.asList(new Exp(1.0), new Exp(2.0)), new double[]{0.6, 0.4});
queue.setService(jobClass, prior);
SolverPosterior solver = new SolverPosterior(model, m -> new SolverMVA(m));
AvgTable avgTable = solver.getAvgTable();

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`public interface`	`SolverPosterior.SolverFactory`	Functional interface for creating solvers.
`public class`	`SolverPosterior.PriorInfo`	Information about a detected Prior distribution.
`public class`	`SolverPosterior.PosteriorTableRow`	Row in the posterior table.
`public class`	`SolverPosterior.PosteriorTable`	Table containing per-alternative posterior results.
`public class`	`SolverPosterior.EmpiricalCDF`	Empirical CDF representing a discrete posterior distribution.

Field Summary

Fields
Modifier and Type	Field	Description
`protected Network`	`originalModel`
`protected SolverPosterior.SolverFactory`	`solverFactory`
`protected SolverPosterior.PriorInfo`	`priorInfo`
`protected SolverResult`	`aggregatedResult`
`protected Array<Network>`	`ensemble`
`protected Array<NetworkSolver>`	`solvers`
`protected Map<Integer, Map<Integer, SolverResult>>`	`results`
`protected ExecutorService`	`threadPool`
`protected int`	`numThreads`
`public Model`	`model`
`public String`	`name`
`public SolverOptions`	`options`
`public SolverResult`	`result`
`public boolean`	`enableChecks`
`public Random`	`random`

Constructor Summary

Constructors
Constructor	Description
`SolverPosterior(Network model, SolverPosterior.SolverFactory solverFactory)`	Creates a SolverPosterior with the given model and solver factory.
`SolverPosterior(Network model, SolverPosterior.SolverFactory solverFactory, SolverOptions options)`	Creates a SolverPosterior with solver options.

Enum Constant Summary

Enum Constants
Enum Constant	Description

Method Summary

Modifier and Type	Method	Description
`static SolverOptions`	`defaultOptions()`	Returns default solver options.
`SolverPosterior.PriorInfo`	`detectPrior()`	Detects Prior distributions in the model.
`boolean`	`hasPriorDistribution()`	Checks if the model has a Prior distribution.
`int`	`getNumAlternatives()`	Returns the number of alternatives in the Prior.
`int`	`getNumberOfModels()`
`Network`	`deepCopyNetwork(Network original)`	Deep copies a Network using serialization.
`void`	`init()`
`void`	`pre(int it)`
`SolverResult`	`analyze(int it, int e)`
`void`	`post(int it)`
`void`	`finish()`
`boolean`	`converged(int it)`
`void`	`aggregateResults()`	Aggregates results from all alternatives using prior weights.
`AvgTable`	`getEnsembleAvg()`
`AvgTable`	`getAvgTable()`	Returns the prior-weighted average results.
`SolverPosterior.PosteriorTable`	`getPosteriorTable()`	Returns a table with per-alternative results and probabilities.
`SolverPosterior.EmpiricalCDF`	`getPosteriorDist(String metric, Station station, JobClass jobClass)`	Returns the posterior distribution for a specific metric at a station/class.
`boolean`	`supports(Network model)`	Checks if this solver supports the given network model.
`void`	`runAnalyzer()`	Runs the posterior analysis.

Methods inherited from class jline.solvers.EnsembleSolver
ensembleAvg, getNumThreads, iterate, numThreads, numberOfModels, printEnsembleAvgTables, printEnsembleAvgTs, setNumThreads
Methods inherited from class jline.solvers.Solver
getName, getOptions, getResults, hasResults, isJavaAvailable, isValidOption, listValidOptions, parseOptions, parseOptions, reset, resetRandomGeneratorSeed, runAnalyzerChecks, setChecks, setOptions
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - SolverPosterior
```
SolverPosterior(Network model, SolverPosterior.SolverFactory solverFactory)
```
    Creates a SolverPosterior with the given model and solver factory.
    
    Parameters:
    
    model - the network model containing Prior distributions
    
    solverFactory - function to create solvers for each alternative
  - SolverPosterior
```
SolverPosterior(Network model, SolverPosterior.SolverFactory solverFactory, SolverOptions options)
```
    Creates a SolverPosterior with solver options.
    
    Parameters:
    
    model - the network model
    
    solverFactory - function to create solvers
    
    options - solver options
- Method Detail
  - defaultOptions
```
 static SolverOptions defaultOptions()
```
    Returns default solver options.
  - detectPrior
```
 SolverPosterior.PriorInfo detectPrior()
```
    Detects Prior distributions in the model. Currently supports only a single Prior per model.
    
    Returns:
    
    PriorInfo for the detected Prior, or null if none found
  - hasPriorDistribution
```
 boolean hasPriorDistribution()
```
    Checks if the model has a Prior distribution.
  - getNumAlternatives
```
 int getNumAlternatives()
```
    Returns the number of alternatives in the Prior.
  - getNumberOfModels
```
 int getNumberOfModels()
```
  - deepCopyNetwork
```
 Network deepCopyNetwork(Network original)
```
    Deep copies a Network using serialization.
  - init
```
 void init()
```
  - pre
```
 void pre(int it)
```
  - analyze
```
 SolverResult analyze(int it, int e)
```
  - post
```
 void post(int it)
```
  - finish
```
 void finish()
```
  - converged
```
 boolean converged(int it)
```
  - aggregateResults
```
 void aggregateResults()
```
    Aggregates results from all alternatives using prior weights.
  - getEnsembleAvg
```
 AvgTable getEnsembleAvg()
```
  - getAvgTable
```
 AvgTable getAvgTable()
```
    Returns the prior-weighted average results.
  - getPosteriorTable
```
 SolverPosterior.PosteriorTable getPosteriorTable()
```
    Returns a table with per-alternative results and probabilities.
    
    Returns:
    
    PosteriorTable with all alternatives
  - getPosteriorDist
```
 SolverPosterior.EmpiricalCDF getPosteriorDist(String metric, Station station, JobClass jobClass)
```
    Returns the posterior distribution for a specific metric at a station/class.
    
    Parameters:
    
    metric - the metric name ("Q", "U", "R", "T", "A")
    
    station - the station
    
    jobClass - the job class
    
    Returns:
    
    EmpiricalCDF representing the posterior distribution
  - supports
```
 boolean supports(Network model)
```
    Checks if this solver supports the given network model. Default implementation returns true; subclasses should override to provide specific feature validation.
    
    Parameters:
    
    model - the network model to check
    
    Returns:
    
    true if the model is supported, false otherwise
  - runAnalyzer
```
 void runAnalyzer()
```
    Runs the posterior analysis.

Class SolverPosterior

Nested Class Summary

Field Summary

Constructor Summary

Enum Constant Summary

Method Summary

Methods inherited from class jline.solvers.EnsembleSolver

Methods inherited from class jline.solvers.Solver

Methods inherited from class java.lang.Object

Constructor Detail

SolverPosterior

SolverPosterior

Method Detail

defaultOptions

detectPrior

hasPriorDistribution

getNumAlternatives

getNumberOfModels

deepCopyNetwork

init

pre

analyze

post

finish

converged

aggregateResults

getEnsembleAvg

getAvgTable

getPosteriorTable

getPosteriorDist

supports

runAnalyzer