Assessment Functions
- pydtmc.assess_first_order(possible_states, sequence, significance=0.05)[source]
The function verifies whether the given sequence can be associated to a first-order Markov process.
- Parameters:
- Raises:
ValidationError
– if any input argument is not compliant.- Return Type:
- pydtmc.assess_homogeneity(possible_states, sequences, significance=0.05)[source]
The function verifies whether the given sequences belong to the same Markov process.
- Parameters:
- Raises:
ValidationError
– if any input argument is not compliant.- Return Type:
- pydtmc.assess_markov_property(possible_states, sequence, significance=0.05)[source]
The function verifies whether the given sequence holds the Markov property.
- Parameters:
- Raises:
ValidationError
– if any input argument is not compliant.- Return Type:
- pydtmc.assess_stationarity(possible_states, sequence, blocks=1, significance=0.05)[source]
The function verifies whether the given sequence is stationary.
- Parameters:
possible_states (
List
[str
]) – the possible states of the process.sequence (
Union
[List
[int
],List
[str
]]) – the observed sequence of states.blocks (
int
) – the number of blocks in which the sequence is divided.significance (
float
) – the p-value significance threshold below which to accept the alternative hypothesis.
- Raises:
ValidationError
– if any input argument is not compliant.- Return Type:
- pydtmc.assess_theoretical_compatibility(mc, sequence, significance=0.05)[source]
The function verifies whether the given empirical sequence is statistically compatible with the given theoretical Markov process.
- Parameters:
- Raises:
ValidationError
– if any input argument is not compliant.- Return Type: