Inductive inference

Female sexual development and behaviour in mammals occurs by default and requires no ovarian secretion, and it is only in genetic males that the testis can secrete hormones which destroy this female pattern and superimpose that of the male. Sexual differentiation is not so well defined in fish, and larval exposure to both synthetic estrogens and androgens is widely used in aquaculture to produce monosex cultures. Endocrine disruption of sexual differentiation in fish may therefore reflect both the complexity and diversity of such processes between different species. Some care is required in use of the terms hermaphrodite and sex-reversal since a true hermaphrodite has both functional testes and ovaries and a sex-reversed fish is fully functional as its final sex—both produce the appropriate viable gametes. Such functional sex-reversal is not possible in mammals, but in some species of fish it is the normal developmental pattern. In most of the cases of hermaphroditism or sex-reversal reported in the non-scientific press, there is evidence only for a few ovarian follicles within a functional testis. This may be considered as feminisation or a form of intersex, and is very clearly endocrine disruption, but it is certainly neither sex-reversal nor hermaphroditism. In some cases the terms have even been used to infer induction of a single female characteristic such as production of yolk-protein by males. 

Folgerung, /. conclusion, inference, induction. Folge wirkung,/. resultant, consequence, -zeit, /. period following, folglich, adv. consequently, folgsam, a. obedient. 

From sensory-accessible data (effects) one can infer (inductively reason) to the existence of an object as the cause of the effects, whose existence can then be verified directly through the senses (or senses enhanced with instrumentation). The character of such an inference is natural scientific, and an example of such an object is the neutrino. 

The secret to success has been to learn from data and from experiments. Chemists have done a series of experiments, have analyzed them, have looked for common features and for those that are different, have developed models that made it po.ssiblc to put these observation.s into a systematic ordering scheme, have made inferences and checked them with new experiments, have then confirmed, rejected, or relined their models, and so on. This process is called inductive learning (Figure 1 -1), a method chemists have employed from the veiy beginnings ol chcmistiy. 

There is, however, another type of learning inductive learning. From a series of observations inferences are made to predict new observations. In order to be able to do this, the observations have to be put into a scheme that allows one to order them, and to recognize the features these observations have in common and the essential features that are different. On the basis of these observations a model of the principles that govern these observations must be built such a model then allows one to make predictions by analogy. 

Quantitative aluminum deterrninations in aluminum and aluminum base alloys is rarely done. The aluminum content is generally inferred as the balance after determining alloying additions and tramp elements. When aluminum is present as an alloying component in alternative alloy systems it is commonly deterrnined by some form of spectroscopy (qv) spark source emission, x-ray fluorescence, plasma emission (both inductively coupled and d-c plasmas), or atomic absorption using a nitrous oxide acetylene flame. 

Induction period measurements can also be used to determine interfacial tensions. To validate the values inferred, however, it is necessary to compare the results with an independent source. Hurley etal. (1995) achieved this for Cyanazine using a dynamic contact angle analyser (Calm DCA312). Solid-liquid interfacial tensions estimated from contact angle measurements were in the range 5-12 mJ/m which showed closest agreement with values (4—20mJ/m ) obtained from the log-log plots of induction time versus supersaturation based on the assumption of — tg. 

Jantke, K., Analogical and Inductive Inference. Springer-Verlag, Berlin, 1989. 

In the following sections we propose typical methods of unsupervised learning and pattern recognition, the aim of which is to detect patterns in chemical, physicochemical and biological data, rather than to make predictions of biological activity. These inductive methods are useful in generating hypotheses and models which are to be verified (or falsified) by statistical inference. Cluster analysis has... 

For Gr, < 920, mass transfer could be represented by the forced-convection correlation and for Gr, > 920, by the free-convection correlation ofFenech and Tobias (F3). Tobias and Hickman (T2) also inferred the existence of cellular vortex flow near the electrode from deposition patterns, the induction length for this behavior agreeing with Eq. (44). 

Alex Rosenberg We all recognise, I think, that correlation is not causation and is at most a symptom for either a causal relation, or for joint causes, or joint effects of a common cause, and the inference from correlation to causation is always an inductive one. 

Observe that we have in this procedure worked out some of the steps previously left to the THEOREM PROVER, The previous procedure involves having the progranmer select a set of inductive assertions and critical points, and then feed this into the computer parts a VERIFICATION CONDITION GENERATOR and a THEOREM PROVER. In this alternative construction we still need inductive assertions as the nature of the Rule of Iteration for WHILE statements shows. Now the inductive assertions are fed directly into the THEOREM PROVER which las been augmented by the special axioms and rules D0,D1,D2,D3 and D4 in addition to all of the usual arithmetic axioms, rules of inference, rules for handling identities and special axioms for the primitives in question (such as the factorial axioms in our example). In effect the THEOREM PROVER works backwards from the output condition and the various inductive assertions using DO - D3 to find what amounts to path verification conditions -... 

From a cholesteric induction experiment, one can obtain chiral information on the induced cholesteric (namely, pitch and handedness) and therefore the helical twisting power of the dopant in that solvent (at a certain temperature). If a model or molecular theory relating molecular chirality to mesophase chirality is available, one can infer stereochemical information about the dopant (absolute configuration, preferred conformation). 

Hacking, Ian. The Emergence of Probability A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference. Cambridge Cambridge University Press, 1975. 

Luminescence is a well-established class of analytical spectroscopic techniques where a species emits light after excitation. Emission is an elecnonic nansition from an excited state as opposed to the ground state as is the case in most other spectroscopies. Photoluminescence, or light-induced fluorescence (LIE), is the most common route to induce emission where sufficient incident photons of a particular energy excite the target species via absorption. Although less common, nomadiative excitation can also occur via a chemical reaction termed chemiluminescence. Unless otherwise stated, the terms luminescence and fluorescence within this review infers excitation by light induction. 

The sampling distribution determined in the previous section is an example of a deductive use of probability. Given that the probability of an occurrence of a one or two is known, we were able to deduce the probability of the outcomes that could arise if the die was tossed 10 times. In medical research, however we do not know what the true probability (response probability) is. Ours is the reverse problem, we observe a response rate, for example, 23 out of 80 patients respond positively to a given treatment, and want to infer what the true population response rate is. The requirement is to be able to make inductive probability statements. 

Bayesian statistics are applicable to analyzing uncertainty in all phases of a risk assessment. Bayesian or probabilistic induction provides a quantitative way to estimate the plausibility of a proposed causality model (Howson and Urbach 1989), including the causal (conceptual) models central to chemical risk assessment (Newman and Evans 2002). Bayesian inductive methods quantify the plausibility of a conceptual model based on existing data and can accommodate a process of data augmentation (or pooling) until sufficient belief (or disbelief) has been accumulated about the proposed cause-effect model. Once a plausible conceptual model is defined, Bayesian methods can quantify uncertainties in parameter estimation or model predictions (predictive inferences). Relevant methods can be found in numerous textbooks, e.g., Carlin and Louis (2000) and Gelman et al. (1997). 

The meaning of n can be inferred from Eq. (5.4) where a measures, in a way, the sensitivity of charge variations to substituent effects. Small n values indicate strong substituent effects. If inductive effects did no exist, the charges would be those corresponding to n = oo (i.e., a = 0), and all H atoms would carry the same charge. No theoretical method leads to this extreme result. 

The cancers induced by both t3q>es of agents are individually indistinguishable from those induced by other causes. Hence, their induction can be inferred only on statistical grounds i.e., from analysis of a dose-dependent increase in their frequency in exposed populations. 

It was long believed that bacteria were unique in their ability to denitrify. However, Shoun and Tanimoto (1991) and Shoun et al., (1989) demonstrated that the fungus, Fusarium oxysporum, could be induced to synthesize an enzyme system capable of the anaerobic reduction of nitrite to N2O. Induction occurred under conditions of low oxygen concentrations in the presence of nitrate or nitrite. One and pethaps the only component of this nitrite reductase system is a unique, soluble cytochrome P-450 (P-450dNIR), which is more similar in its cDNA-inferred amino acid sequence to soluble, bacterial P-450 enzymes (espe-... 

---