“I hardly think a girl is much of a threat. I presume you searched her for weapons? But if she attempts to suffocate me with her straw mattress, I promise to call out for help.”
― Mary Hoffman, quote from Stad van maskers
“If I had known, do you think I should have let her get away with this mad plan? That I should have let her rob me of my child? No, I should have taken you myself and hidden with you in some far-off land and never seen her again rather than agree to such an unnatural scheme”
― Mary Hoffman, quote from Stad van maskers
“Dia... het is zo. Ik begrijp er niet veel van, maar ik geloof je wél. Je komt uit een grote stad hier heel ver vandaan, waar je heel erg ziek bent, en hier ben je opeens gezond en wel. Hoe bestaat dat? - Arianna”
― Mary Hoffman, quote from Stad van maskers
“Een dolk en een masker,' fluisterde Lucien. 'Over een paar maanden zul je nog gevaarlijker zijn dan je nu al bent.”
― Mary Hoffman, quote from Stad van maskers
“De duchessa is dood. - Silvia”
― Mary Hoffman, quote from Stad van maskers
“Ik heb mijn besluit genomen. - Arianna”
― Mary Hoffman, quote from Stad van maskers
Mary Hoffman
Born place: in The United Kingdom
Born date January 1, 1945
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“I can’t tell him no. Some people have a relationship that’s sunshine and roses. Ours is darkness and vengeance.”
― Annika Martin, quote from Prisoner
“We make patterns, we share moments.”
― Jenny Downham, quote from Now is Good
“To understand my doctor’s error, let’s employ Bayes’s method. The first step is to define the sample space. We could include everyone who has ever taken an HIV test, but we’ll get a more accurate result if we employ a bit of additional relevant information about me and consider only heterosexual non-IV-drug-abusing white male Americans who have taken the test. (We’ll see later what kind of difference this makes.) Now that we know whom to include in the sample space, let’s classify the members of the space. Instead of boy and girl, here the relevant classes are those who tested positive and are HIV-positive (true positives), those who tested positive but are not positive (false positives), those who tested negative and are HIV-negative (true negatives), and those who tested negative but are HIV-positive (false negatives). Finally, we ask, how many people are there in each of these classes? Suppose we consider an initial population of 10,000. We can estimate, employing statistics from the Centers for Disease Control and Prevention, that in 1989 about 1 in those 10,000 heterosexual non-IV-drug-abusing white male Americans who got tested were infected with HIV.6 Assuming that the false-negative rate is near 0, that means that about 1 person out of every 10,000 will test positive due to the presence of the infection. In addition, since the rate of false positives is, as my doctor had quoted, 1 in 1,000, there will be about 10 others who are not infected with HIV but will test positive anyway. The other 9,989 of the 10,000 men in the sample space will test negative. Now let’s prune the sample space to include only those who tested positive. We end up with 10 people who are false positives and 1 true positive. In other words, only 1 in 11 people who test positive are really infected with HIV.”
― Leonard Mlodinow, quote from The Drunkard's Walk: How Randomness Rules Our Lives
“As I swallowed the last bite of my bagel, my fear gave way to anger. “And just what the hell did he mean by ‘my erratic behavior’?”
Ian just looked at me. Yasha and Calvin stared straight ahead and didn’t say a word.
I glared at all of them.”
― Lisa Shearin, quote from The Grendel Affair
“Revenge is a tricky beast. Her claws face both ways. I don’t mind a few more scars. They’ll be unnoticed among the rest.”
― Kim Harrison, quote from The Witch With No Name
Shattered Glass
Metamorphosis
Women of the Silk
Killer Spirit
Hood
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