Proposes a move in state space.
Operator that does proposals that count for one step or more steps in the MCMC
Run MCMC on different gene tree parts of the model in parallel before combining them in a single Gibbs move
bactrian, includeRealParameters, exclude, include, logsum, distribution, unthread, chainLength, chainCoverage, threads, otherState, learning, burnin, schedule, nregression, targetCPU, targetWeight, runtime, speciesTree, weight
| type: java.lang.Boolean |
| flag to indicate that bactrian operators should be used where possible |
| Optional input. Default: true |
| type: java.lang.Boolean |
| flag to include Real Parameters for each of the partitions in the analysis |
| Optional input. Default: true |
| type: beast.base.inference.StateNode*** |
| parameters to ensure are not operated on |
| Optional input |
| type: beast.base.inference.StateNode*** |
| parameters to add to the state, in case they are not automatically detected |
| Optional input |
| type: beast.base.inference.StateNode*** |
| these parameters, if operated on, will have their total sum respected by the avmn operator |
| Optional input |
| type: starbeast3.operators.ParallelDistSet*** |
| Distribution on a tree conditinionally independent from all other distributions given the state of the restof parameter space. |
| Optional input |
| type: java.lang.Boolean |
| flag to convert ThreadedTreeLikelihood back into TreeLikelihood when this is called |
| Optional input. Default: false |
| type: java.lang.Long |
| Length of the MCMC chain: each individual ParallelMCMC performs chainLength/nrOfThreads samples |
| Either this, or chainCoverage needs to be specified |
| type: java.lang.Double |
| The MCMC chain length is the coverage times the number of parameters |
| Either this, or chainLength needs to be specified |
| type: java.lang.Integer |
| maximum number of threads to use, if less than 1 the number of threads in BeastMCMC is used (default -1) |
| Optional input. Default: -1 |
| type: beast.base.inference.State |
| main state containing all statenodes for this analysis |
| Optional input |
| type: java.lang.Boolean |
| Learn whether to parallelise (n threads) or not (1 thread 1 operator) |
| Optional input. Default: true |
| type: java.lang.Integer |
| How many operator calls before thread learning kicks in. Learning will begin after chainLength regression. |
| Optional input. Default: 10000 |
| type: starbeast3.core.OperatorScheduleRecalculator |
| Operator schedule (if learning is applied) |
| Optional input |
| type: java.lang.Integer |
| Number of MCMC chainLengths vs runtimes to sample in order to learn chainLengths, for load balancing. Set to <5 to skip the training. |
| Optional input. Default: 200 |
| type: java.lang.Double |
| Proportion of threads allocated (if > 1) that should be spent on MCMC, as opposed to overhead.Larger targetCPU will mean longer chains and lower operator weight. If targetCPU=0, then the load balancing will match the slowest thread. Set nregression=0 to omit this step. |
| Optional input. Default: 0.8 |
| type: java.lang.Double |
| Target effective weight of this operator, to be learned if regression is applied. The effective weight is the operator weight * chainLength sum. Set this to 0 (or nregression=0) to omit this step. |
| Optional input. Default: 0.0 |
| type: java.lang.Double |
| Max runtime of MCMC chains during training (only applicable if load balancing is being trained). If this isset to -1, then chain lengths are sampled instead of runtimes. |
| Optional input. Default: -1.0 |
| type: beast.base.evolution.tree.Tree |
| an optional dummy input so that beauti can load the template (hack) |
| Optional input |
| type: java.lang.Double |
| weight with which this operator is selected |
| Required input |