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| ===== Homework ===== | ===== Homework ===== | ||
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| + | ===== Final Essay ===== | ||
| + | Here are some notes I wrote on probability measure spaces for my final essay. | ||
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| ===== Other Notes/ | ===== Other Notes/ | ||
| ==== Uncountable Coverings ==== | ==== Uncountable Coverings ==== | ||
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| One difference between the Riemann and Lebesgue integrals is that the Riemann integral is only defined for bounded functions on bounded domains. Since the Lebesgue integral is not subject to these restrictions, | One difference between the Riemann and Lebesgue integrals is that the Riemann integral is only defined for bounded functions on bounded domains. Since the Lebesgue integral is not subject to these restrictions, | ||
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| + | ==== Lebesgue Measure Theory Summary ==== | ||
| + | Tao's approach to Lebesgue measure theory starts by defining a $\textbf{simple function}$, which takes on finitely many possible values. We first define how to integrate a simple function, and then use that to define how to integrate measurable functions. | ||
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| + | Simply put, the integral of a simple function is defined to be the product of each possible value with the measure of the subset of the region of integration that corresponds to the possible value. Then, to integrate non-negative measurable functions, we define the integral of a measurable function f to be the supremum of the integral of non-negative simple functions that are less than or equal to f. | ||
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| + | Some of the key results for Lebesgue integration of non-negative functions include: | ||
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| + | $\textbf{Lebesgue Monotone Convergence Theorem}$: For a sequence $(f_n)$ of functions from $\Omega \rightarrow \mathbb{R}$, | ||
| + | $$0 \leq f_1(x) \leq f_2(x) \leq ...$$ | ||
| + | we have that | ||
| + | $$0 \leq \int_{\Omega} f_1(x) \leq \int_{\Omega} f_2(x) \leq ...$$ | ||
| + | and | ||
| + | $$\int_{\Omega} \sup{f_n} = \sup{\int_{\Omega} f_n}$$ | ||
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| + | The proof of the inequality in the $\geq$ direction is simple. For the opposite direction, we first use the definition of Lebesgue integration to look at simple functions less than or equal to $\sup{f_n}$. Then, we use the trick where we show the inequality holds after cutting by a factor of an arbitrarily small epsilon. Finally, after cutting our simple function $s$ to $(1-\epsilon)s$, | ||
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| + | $\textbf{Interchange of Integration and Addition}$: $\int_{\Omega} (f+g) = \int_{\Omega} f + \int_{\Omega} g$ | ||
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| + | Tao's proof of this theorem is easier to follow (at least in my opinion) than Pugh' | ||
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| + | $\textbf{Fatou' | ||
| + | $$\int_{\Omega} \lim_{n \rightarrow \infty} \inf{f_n} \leq \lim_{n\rightarrow \infty} \inf{\int_{\Omega} f_n}$$ | ||
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| + | The key idea in the proof is rewriting the liminf on the LHS as a sup, and apply the Monotone Convergence Theorem. | ||
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| + | Finally, for integration of measurable functions that are not necessarily non-negative, | ||
| + | $$f^+ = \max{(f, 0)}, f^- = -\min{(f, 0)}$$ | ||
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| + | Then, we define | ||
| + | $$\int f = \int f^+ - \int f^-$$ | ||
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| + | Many of the properties of the integral of non-negative functions can be generalized to the general Lebesgue integral. | ||
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| + | A key theorem involving absolutely integrable functions is: | ||
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| + | $\textbf{Lebesgue Dominated Convergence Theorem}$: | ||
| + | $$\int_{\Omega} \lim_{n\rightarrow\infty} f_n = \lim_{n\rightarrow\infty}\int_{\Omega}f_n$$ | ||
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| + | To prove this theorem, we use Fatou' | ||
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| + | ==== Littlewood' | ||
| + | Littlewood' | ||
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| + | These three principles share a common theme that removing an arbitrarily small set can lead to nice properties, or that measurable functions/ | ||