Lower Previsions¶

class improb.lowprev.LowPrev

Abstract base class for working with arbitrary lower previsions.

dominates(gamble, other_gamble, event=True, algorithm=None)

Does gamble dominate other_gamble in lower prevision?

Parameters: gamble (dict or similar; see Gambles) – The left hand side gamble. other_gamble (dict or similar; see Gambles) – The right hand side gamble. event (list or similar; see Events) – The event to condition on. algorithm (str) – The algorithm to use (the default value uses the most efficient algorithm). True if gamble dominates other_gamble, False otherwise. bool
get_extend_domain()

Get largest possible domain to which the lower prevision can be extended.

get_lower(gamble, event=True, algorithm=None)

Return the lower expectation for gamble conditional on event via natural extension.

Parameters: gamble (dict or similar; see Gambles) – The gamble whose upper expectation to find. event (list or similar; see Events) – The event to condition on. algorithm (str) – The algorithm to use (the default value uses the most efficient algorithm). The lower bound for this expectation, i.e. the natural extension of the gamble. float or Fraction
get_upper(gamble, event=True, algorithm=None)

Return the upper expectation for gamble conditional on event via natural extension.

Parameters: gamble (dict or similar; see Gambles) – The gamble whose upper expectation to find. event (list or similar; see Events) – The event to condition on. algorithm (str) – The algorithm to use (None for the most efficient algorithm). The upper bound for this expectation, i.e. the natural extension of the gamble. float or Fraction
is_avoiding_sure_loss(algorithm=None)

No Dutch book? Does the lower prevision avoid sure loss?

Returns: True if avoids sure loss, False otherwise. bool
is_coherent(algorithm=None)

Do all assessments coincide with their natural extension? Is the lower prevision coherent?

Parameters: algorithm (str) – The algorithm to use (the default value uses the most efficient algorithm). True if coherent, False otherwise. bool
is_linear(algorithm=None)

Is the lower prevision a linear prevision? More precisely, we check that the natural extension is linear on the linear span of the domain of the lower prevision.

Parameters: algorithm (str) – The algorithm to use (the default value uses the most efficient algorithm). True if linear, False otherwise. bool
pspace

An PSpace representing the possibility space.

Introduction

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Polyhedral Lower Previsions