Quick Reference

Essential Imports

from stochlab.core import StateSpace, Path, SimulationResult
from stochlab.models import MarkovChain
import numpy as np

Basic Workflow

1. Create State Space

states = StateSpace(["A", "B", "C"])

2. Build Markov Chain

P = np.array([[0.7, 0.2, 0.1],
              [0.3, 0.4, 0.3], 
              [0.1, 0.1, 0.8]])
mc = MarkovChain.from_transition_matrix(["A", "B", "C"], P)

3. Simulate Paths

# Single path
path = mc.sample_path(T=100, x0="A")

# Multiple paths
result = mc.simulate_paths(n_paths=1000, T=100)

4. Analyze Results

# Convert to DataFrame
df = result.to_dataframe()

# State distribution at time t
dist = result.state_distribution(t=50)

# Access individual paths
first_path = result.paths[0]
final_state = first_path[-1]

Key Methods

Class	Method	Purpose
`StateSpace`	`index(state)`	Get index of state
`StateSpace`	`state(idx)`	Get state at index
`MarkovChain`	`sample_path(T, x0)`	Generate single trajectory
`MarkovChain`	`simulate_paths(n_paths, T)`	Monte Carlo simulation
`Path`	`path[i]`	Get state at time i
`SimulationResult`	`to_dataframe()`	Convert to pandas DataFrame
`SimulationResult`	`state_distribution(t)`	Empirical distribution