Code for reproducing the experiments in the paper submitted to ICML
"Sequential Likelihood-Free Inference with Implicit Surrogate Proposal"
python: 3.8
pyTorch: 1.7.1
pyknos 0.14.0
statsmodels 0.12.2
pyro 1.5.2
The followings are the commands for the experiments.
1) Shubert Simulation
1-1) SMC-ABC
python3.6 SBI.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --algorithm SMC --nsfTailBound 10
1-2) APT
python3.6 SBI.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --algorithm SNPE --nsfTailBound 10
1-3) SNL + Slice sampler with a single chain
python3.6 SNL.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 1
1-4) SNL + Slice sampler with 10 chains
python3.6 SNL.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 10
1-5) SNL + Metropolis-Hastings sampler with a single chain
python3.6 SNL.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 1
1-6) SNL + Metropolis-Hastings sampler with 100 chains
python3.6 SNL.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 100
1-7) AALR + Slice sampler with a single chain
python3.6 AALR.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 1
1-8) AALR + Slice sampler with 10 chains
python3.6 AALR.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 10
1-9) AALR + Metropolis-Hastings sampler with a single chain
python3.6 AALR.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 1
1-10) AALR + Metropolis-Hastings sampler with 10 chains
python3.6 AALR.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod no --device cuda:0 --numChains 100
1-11) SNL + Implicit Surrogate Proposal
python3.6 SNL.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod rkl --device cuda:0
1-12) AALR + Implicit Surrogate Proposal
python3.6 AALR.py --simulation shubert --thetaDim 2 --xDim 2 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 5000 --numRound 10 --numModes 18 --posteriorInferenceMethod rkl --device cuda:0
2) SLCP-16 Simulation
2-1) SMC-ABC
python3.6 SBI.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --algorithm SMC
2-2) APT
python3.6 SBI.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --algorithm SNPE
2-3) SNL + Slice sampler with a single chain
python3.6 SNL.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 1
2-4) SNL + Slice sampler with 10 chains
python3.6 SNL.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 10
2-5) SNL + Metropolis-Hastings sampler with a single chain
python3.6 SNL.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 1
2-6) SNL + Metropolis-Hastings sampler with 100 chains
python3.6 SNL.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 100
2-7) AALR + Slice sampler with a single chain
python3.6 AALR.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 1
2-8) AALR + Slice sampler with 10 chains
python3.6 AALR.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 10
2-9) AALR + Metropolis-Hastings sampler with a single chain
python3.6 AALR.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 1
2-10) AALR + Metropolis-Hastings sampler with 10 chains
python3.6 AALR.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod no --device cuda:0 --numChains 100
2-11) SNL + Implicit Surrogate Proposal
python3.6 SNL.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod rkl --device cuda:0
2-12) AALR + Implicit Surrogate Proposal
python3.6 AALR.py --simulation complexPosterior --thetaDim 5 --xDim 50 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 16 --posteriorInferenceMethod rkl --device cuda:0
3) SLCP-256 Simulation
3-1) SMC-ABC
python3.6 SBI.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --algorithm SMC
3-2) APT
python3.6 SBI.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --algorithm SNPE
3-3) SNL + Slice sampler with a single chain
python3.6 SNL.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --thinning 1
3-4) SNL + Slice sampler with 10 chains
python3.6 SNL.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 10 --thinning 1
3-5) SNL + Metropolis-Hastings sampler with a single chain
python3.6 SNL.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 1
3-6) SNL + Metropolis-Hastings sampler with 100 chains
python3.6 SNL.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 100
3-7) AALR + Slice sampler with a single chain
python3.6 AALR.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 1
3-8) AALR + Slice sampler with 10 chains
python3.6 AALR.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 10
3-9) AALR + Metropolis-Hastings sampler with a single chain
python3.6 AALR.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 1
3-10) AALR + Metropolis-Hastings sampler with 10 chains
python3.6 AALR.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod no --device cuda:0 --numChains 100
3-11) SNL + Implicit Surrogate Proposal
python3.6 SNL.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod rkl --device cuda:0
3-12) AALR + Implicit Surrogate Proposal
python3.6 AALR.py --simulation complexPosterior_ver2 --thetaDim 8 --xDim 40 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS True --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numModes 256 --posteriorInferenceMethod rkl --device cuda:0
4) M/G/1
4-1) SMC-ABC
python3.6 SBI.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numRound 10 --numTime 50 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --algorithm SMC --nsfTailBound 20
4-2) APT
python3.6 SBI.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numRound 10 --numTime 50 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --algorithm SNPE --nsfTailBound 20
4-3) SNL + Slice sampler with a single chain
python3.6 SNL.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 20
4-4) SNL + Slice sampler with 10 chains
python3.6 SNL.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 10 --nsfTailBound 20
4-5) SNL + Metropolis-Hastings sampler with a single chain
python3.6 SNL.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 20
4-6) SNL + Metropolis-Hastings sampler with 100 chains
python3.6 SNL.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 100 --nsfTailBound 20
4-7) AALR + Slice sampler with a single chain
python3.6 AALR.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 20
4-8) AALR + Slice sampler with 10 chains
python3.6 AALR.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 10 --nsfTailBound 20
4-9) AALR + Metropolis-Hastings sampler with a single chain
python3.6 AALR.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 20
4-10) AALR + Metropolis-Hastings sampler with 10 chains
python3.6 AALR.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod no --device cuda:0 --numChains 100 --nsfTailBound 20
4-11) SNL + Implicit Surrogate Proposal
python3.6 SNL.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod rkl --device cuda:0 --nsfTailBound 20
4-12) AALR + Implicit Surrogate Proposal
python3.6 AALR.py --simulation mg1 --thetaDim 3 --xDim 5 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 100 --numTime 50 --numRound 10 --numModes 1 --posteriorInferenceMethod rkl --device cuda:0 --nsfTailBound 20
5) Competitive Lotka Volterra
5-1) SMC-ABC
python3.6 SBI.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numTime 1000 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --algorithm SMC --nsfTailBound 3
5-2) APT
python3.6 SBI.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numRound 10 --numTime 1000 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --algorithm SNPE --nsfTailBound 3
5-3) SNL + Slice sampler with a single chain
python3.6 SNL.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 3
5-4) SNL + Slice sampler with 10 chains
python3.6 SNL.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 10 --nsfTailBound 3
5-5) SNL + Metropolis-Hastings sampler with a single chain
python3.6 SNL.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 3
5-6) SNL + Metropolis-Hastings sampler with 100 chains
python3.6 SNL.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 100 --nsfTailBound 3
5-7) AALR + Slice sampler with a single chain
python3.6 AALR.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 3
5-8) AALR + Slice sampler with 10 chains
python3.6 AALR.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType sbiSliceSampler --burnInMCMC 100 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 10 --nsfTailBound 3
5-9) AALR + Metropolis-Hastings sampler with a single chain
python3.6 AALR.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 1 --nsfTailBound 3
5-10) AALR + Metropolis-Hastings sampler with 10 chains
python3.6 AALR.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod no --device cuda:0 --numChains 100 --nsfTailBound 3
5-11) SNL + Implicit Surrogate Proposal
python3.6 SNL.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod rkl --device cuda:0 --nsfTailBound 3
5-12) AALR + Implicit Surrogate Proposal
python3.6 AALR.py --simulation competitiveLotkaVolterra_ver2 --thetaDim 8 --xDim 10 --samplerType MHGaussianMultiChainsSampler --burnInMCMC 1000 --plotMIS False --plotPerformance True --logCount True --simulation_budget_per_round 1000 --numTime 1000 --numRound 10 --numModes 2 --posteriorInferenceMethod rkl --device cuda:0 --nsfTailBound 3