1. A method for estimating a glucose level of a person having diabetes, the method comprising: receiving into a computing device a plurality of measured glucose results from a glucose sensor coupled to the person; using the computing device to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy P A based on the plurality of measured glucose results; using the computing device to estimate the glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the probability of glucose sensor accuracy P A ; and wherein the probability analysis tool comprises a hidden Markov model, wherein: the hidden Markov model has two states: a first state S A indicating the glucose sensor is accurate, and a second state S I indicating the glucose sensor is inaccurate; and the hidden Markov model is configured to determine the probability of glucose sensor accuracy P A , based on a state of the hidden Markov model and the plurality of measured glucose results; and wherein probability of the hidden Markov model transitioning from the first state S A to second the state S I is P A -> I = min ⁡ [ ( 1 - 1 1 + ⅇ - ( Δ ⁢ ⁢ CG + α 1 ) / α 2 ) + ( 1 - 1 1 + ⅇ - ( ⁢ CG + α 3 ) / α 4 ) , 1 ] , where CG is a most-recent measured glucose result, ΔCG is a most-recent change in the plurality of measured glucose results, and α 1 , α 2 , α 3 and α 4 are constants related to characteristics of the glucose sensor.