diff --git a/_sources/book/topology/002-topological-spaces.md b/_sources/book/topology/002-topological-spaces.md new file mode 100644 index 00000000..1422c22d --- /dev/null +++ b/_sources/book/topology/002-topological-spaces.md @@ -0,0 +1,47 @@ +# Topological Spaces + + +Star +Issue +Watch +Follow + +In the previous chapter we saw that in a metric space, continuity of functions is only indirectly determined by the metric itself. +Instead, the structure that determines continuity {prf:ref}`is the set of open sets`. +This motivates the definition of a topological space, which abstracts the notion of open sets from metric spaces. + +## Topologies +First, we define topological spaces. +These are sets equipped with a topology, a collection of subsets which we _define_ to be open. +Unlike in metric spaces, where we first defined open balls and then used them to define open sets, here we define open sets directly, and require they satisfy certain properties. + +:::{prf:definition} Topological space +:label: topology:def-topological-space +A topological space is a set $X,$ called the space, together with a collection $\mathcal{U} \subseteq \mathcal{P}(X)$ of subsets of $X,$ called the topology on $X,$ such that + +1. $\emptyset, X \in \mathcal{U},$ +2. If ${U_i}_{i \in I} \subseteq \mathcal{U},$ then $\bigcup_{i \in I} U_i \in \mathcal{U},$ +3. If $U_1, \dots, U_n \in \mathcal{U},$ then $\bigcap_{i=1}^n U_i \in \mathcal{U}.$ + +The elements of $X$ are called points, and the elements of $\mathcal{U}$ are called open sets. +::: + + +When working with specific spaces, they will often be already be equipped with a metric. +We refer to the topology associated with a given metric as the induced topology. + +:::{prf:definition} Induced topology +:label: topology:def-induced-topology +Let $(X, d)$ be a metric space. +Then, the topology induced by $d$ is the set of all open sets in $X$ with respect to the metric $d.$ +::: + + +We now also re-define continuity in terms of open sets. + +:::{prf:definition} Continuous function +:label: topology:def-continuous-function-topology +Let $f: X \to Y$ be a function between topological spaces. +Then, $f$ is continuous if for every open set $U \subseteq Y,$ the pre-image $f^{-1}(U)$ is an open set in $X.$ +::: + diff --git a/book/mira/000-exercises.html b/book/mira/000-exercises.html index 1a503e19..e645c444 100644 --- a/book/mira/000-exercises.html +++ b/book/mira/000-exercises.html @@ -215,6 +215,7 @@
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  • Topology
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  • Topology
  • diff --git a/book/mira/002-measures.html b/book/mira/002-measures.html index 14367ca2..4c7f3bc1 100644 --- a/book/mira/002-measures.html +++ b/book/mira/002-measures.html @@ -215,6 +215,7 @@
  • Topology
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    Importance-weighted MCMC -

    Definition 80 (Importance weighted MCMC algorithm)

    +

    Definition 83 (Importance weighted MCMC algorithm)

    Given a proposal density \(q\), a target density \(p\) and a sequence of transition kernels \(T_1(x, x'), \dots, T_K(x, x')\) be a sequence of transition kernels such that \(T_k\) leaves \(p\) invariant. Sampling \(x_0 \sim q(x)\) followed by

    @@ -736,7 +737,7 @@

    Annealed Importance Sampling\(q\) and \(p\) as we vary \(\beta\). AIS then proceeds in a similar way to the importance weighted MCMC algorithm we highlighted above, except that it requires that each \(T_k\) leaves \(\pi_k\), instead of \(p\), invariant.

    -

    Definition 81 (Annealed Importance Sampling)

    +

    Definition 84 (Annealed Importance Sampling)

    Given a target density \(p\), a proposal density \(q\) and a sequence \(0 = \beta_0 \leq \dots \leq \beta_K = 1\), define

    diff --git a/book/papers/intro.html b/book/papers/intro.html index a5aa35b1..1ec85610 100644 --- a/book/papers/intro.html +++ b/book/papers/intro.html @@ -79,7 +79,7 @@ - + @@ -215,6 +215,7 @@
  • Topology
  • @@ -473,12 +474,12 @@

    Stream of papers

    previous

    -

    Metric spaces

    +

    Topological Spaces

  • Topology
  • @@ -466,7 +467,7 @@

    Why stochastic differential equations#

    In order to define the stochastic component of the transition rule of a stochastic system, we must define an appropriate noise model. The Wiener process is a stochastic process that is commonly used for this purpose.

    -

    Definition 85 (Wiener process)

    +

    Definition 88 (Wiener process)

    A standard Wiener process over [0, T] is a random variable \(W(t)\) that depends continuously on \(t \in [0, T]\) and satisfies:

      @@ -649,7 +650,7 @@

      Evaluating a stochastic integral#

      The Euler-Maruyama method is the analoge of the Euler method for deterministic integrals, applied to the stochastic case.

      -

      Definition 86 (Euler-Maruyama method)

      +

      Definition 89 (Euler-Maruyama method)

      Given a scalar SDE with drift and diffusion functions \(f\) and \(g\)

      @@ -769,7 +770,7 @@

      Euler-Maruyama methodStrong and weak convergence#

      Since the choice of the number of bins \(N\) of the discretisation affects the accuracy of our method, we are interested in how quickly the approximation converges to the exact solution as a function of \(N\). To do so, we must first define what convergence means in the stochastic case, which leads us to two disctinct notions of convergence, the strong sence and the weak sense.

      -

      Definition 87 (Strong convergence)

      +

      Definition 90 (Strong convergence)

      A method for approximating a stochastic process \(X(t)\) is said to have strong order of convergence \(\gamma\) if there exists a constant such that

      @@ -779,7 +780,7 @@

      Strong and weak convergence\(X_n\) to the exact solution \(X(\tau_n)\) as \(\Delta t \to 0\), in expectation. A weaker condition for convergence is rate at which the expected value of the approximation converges to the true expected value, as \(\Delta t \to 0\), as given below.

      -

      Definition 88 (Weak convergence)

      +

      Definition 91 (Weak convergence)

      A method for approximating a stochastic process \(X(t)\) is said to have weak order of convergence \(\gamma\) if there exists a constant such that

      @@ -798,7 +799,7 @@

      Strong and weak convergence#

      Just as higher order methods for ODEs exist for obtaining refined estimates of the solution, so do methods for SDEs, such as Milstein’s higher order method.

      -

      Definition 89 (Milstein’s method)

      +

      Definition 92 (Milstein’s method)

      Given a scalar SDE with drift and diffusion functions \(f\) and \(g\)

      diff --git a/book/papers/rff/rff.html b/book/papers/rff/rff.html index 15798679..f38156c8 100644 --- a/book/papers/rff/rff.html +++ b/book/papers/rff/rff.html @@ -215,6 +215,7 @@
    1. Topology
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      The RFF approximationThis is also an unbiased estimate of the kernel, however its variance is lower than in the \(M = 1\) case, since the variance of the average of the sum of \(K\) i.i.d. random variables is lower than the variance of a single one of the variables. We therefore arrive at the following algorithm for estimating \(k\).

      -

      Definition 82 (Random Fourier Features)

      +

      Definition 85 (Random Fourier Features)

      Given a translation invariant kernel \(k\) that is the Fourier transform of a probability measure \(p\), we have the unbiased real-valued estimator

      diff --git a/book/papers/score-matching/score-matching.html b/book/papers/score-matching/score-matching.html index 48cdc387..849b9d22 100644 --- a/book/papers/score-matching/score-matching.html +++ b/book/papers/score-matching/score-matching.html @@ -215,6 +215,7 @@
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      The score matching trick\(\psi_\theta(x)\) along with some observed data, to estimate the parameters \(\theta\). We can achieve this by defining the following score matching objective.

      -

      Definition 83 (Score matching objective)

      +

      Definition 86 (Score matching objective)

      Given a data distribution \(p_d(x)\) and an approximating distribution \(p_\theta(x)\) with parameters \(\theta\), we define the score matching objective as

      diff --git a/book/papers/svgd/svgd.html b/book/papers/svgd/svgd.html index 2dc2a827..e89beeac 100644 --- a/book/papers/svgd/svgd.html +++ b/book/papers/svgd/svgd.html @@ -215,6 +215,7 @@
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      Direction of steepest descent#

      Now, if we approximate \(q\) by a finite set of \(N\) particles at locations \(x_n^{(i)}, n = 1, ..., N\), at the \(i^{th}\) iteration, we obtain at the following iterative algorithm.

      -

      Definition 84 (Stein variational gradient descent)

      +

      Definition 87 (Stein variational gradient descent)

      Given a distribution \(p(x)\), a postive definite kernel \(k(x, x')\) and a set of particles with initial positions \(\{x_n^{(0)}\}_{n=1}^N\), Stein variational gradient descent evolves the particles according to

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      Open and closed sets

      next

      -

      Stream of papers

      +

      Topological Spaces

      diff --git a/book/topology/002-topological-spaces.html b/book/topology/002-topological-spaces.html new file mode 100644 index 00000000..9f941124 --- /dev/null +++ b/book/topology/002-topological-spaces.html @@ -0,0 +1,605 @@ + + + + + + + + + + + Topological Spaces — Random Walks + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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      Topological Spaces

      + +
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      Contents

      +
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      Topological Spaces#

      + +

      Star +Issue +Watch +Follow

      +

      In the previous chapter we saw that in a metric space, continuity of functions is only indirectly determined by the metric itself. +Instead, the structure that determines continuity is the set of open sets. +This motivates the definition of a topological space, which abstracts the notion of open sets from metric spaces.

      +
      +

      Topologies#

      +

      First, we define topological spaces. +These are sets equipped with a topology, a collection of subsets which we define to be open. +Unlike in metric spaces, where we first defined open balls and then used them to define open sets, here we define open sets directly, and require they satisfy certain properties.

      +
      +

      Definition 80 (Topological space)

      +
      +

      A topological space is a set \(X,\) called the space, together with a collection \(\mathcal{U} \subseteq \mathcal{P}(X)\) of subsets of \(X,\) called the topology on \(X,\) such that

      +
        +
      1. \(\emptyset, X \in \mathcal{U},\)

      2. +
      3. If \({U_i}_{i \in I} \subseteq \mathcal{U},\) then \(\bigcup_{i \in I} U_i \in \mathcal{U},\)

      4. +
      5. If \(U_1, \dots, U_n \in \mathcal{U},\) then \(\bigcap_{i=1}^n U_i \in \mathcal{U}.\)

      6. +
      +

      The elements of \(X\) are called points, and the elements of \(\mathcal{U}\) are called open sets.

      +
      +

      When working with specific spaces, they will often be already be equipped with a metric. +We refer to the topology associated with a given metric as the induced topology.

      +
      +

      Definition 81 (Induced topology)

      +
      +

      Let \((X, d)\) be a metric space. +Then, the topology induced by \(d\) is the set of all open sets in \(X\) with respect to the metric \(d.\)

      +
      +

      We now also re-define continuity in terms of open sets.

      +
      +

      Definition 82 (Continuous function)

      +
      +

      Let \(f: X \to Y\) be a function between topological spaces. +Then, \(f\) is continuous if for every open set \(U \subseteq Y,\) the pre-image \(f^{-1}(U)\) is an open set in \(X.\)

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      Proof Index

      toc-dfa-pumping-lemma | topology:cauchy-schwarz | topology:def-continuous-function | + topology:def-continuous-function-topology | + topology:def-induced-topology | topology:def-limit-point | topology:def-metric-space | topology:def-norm | topology:def-open-and-closed-balls | topology:def-open-and-closed-subsets | topology:def-open-neighbourhood | + topology:def-topological-space | topology:examples-of-norms | topology:inner-product-space | topology:lemma-closed-set-iff-set-contains-all-its-limit-points | @@ -1291,6 +1295,22 @@

      Proof Index

      topology:def-continuous-function (book/topology/001-metric-spaces) definition   + + topology:def-continuous-function-topology + + + + topology:def-continuous-function-topology (book/topology/002-topological-spaces) + definition +   + + topology:def-induced-topology + + + + topology:def-induced-topology (book/topology/002-topological-spaces) + definition +   topology:def-limit-point @@ -1339,6 +1359,14 @@

      Proof Index

      topology:def-open-neighbourhood (book/topology/001-metric-spaces) definition   + + topology:def-topological-space + + + + topology:def-topological-space (book/topology/002-topological-spaces) + definition +   topology:examples-of-norms diff --git a/search.html b/search.html index 58d2dfef..36b22378 100644 --- a/search.html +++ b/search.html @@ -214,6 +214,7 @@
    57. Topology
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[[31, "topology:def-continuous-function"]], " (Continuous functions are Riemann integrable)": [[2, "theorem-8"]], " (Continuous random variable)": [[17, "definition-2"]], " (Convergence implies sequence eventually in open neighbourhood)": [[31, "topology:lemma-convergence-implies-sequence-eventually-in-open-neighbourhood"]], " (Convergence in distribution to c \\implies convergence in probability to c)": [[20, "theorem-12"]], " (Convergence in distribution)": [[20, "definition-10"]], " (Convergence in mean square \\implies convergence in probability)": [[20, "theorem-3"]], " (Convergence in probability (\\implies) convergence in distribution)": [[20, "theorem-11"]], " (Convergence in probability)": [[20, "definition-2"]], " (Convergent sequence)": [[31, "definition-3"]], " (Convex function)": [[19, "definition-10"]], " (Convolution formula)": [[15, "theorem-6"], [18, "theorem-3"]], " (Correlation between -1 and 1)": [[19, "theorem-3"]], " (Correlation coefficient)": [[19, "definition-2"]], " (Countable sets have outer measure zero)": [[3, "mira:thm:countable-sets-have-measure-zero"]], " (Countable subadditivity)": [[3, "theorem-38"]], " (Covariance)": [[19, "definition-1"]], " (Direction of steepest descent)": [[9, "theorem-1"]], " (Discrete random variable)": [[14, "definition-0"]], " (Distribution function)": [[17, "definition-1"]], " (Equivalent form of J)": [[8, "theorem-2"]], " (Euler-Maruyama method)": [[6, "definition-1"]], " (Event space)": [[13, "definition-0"]], " (Every continuous function is Borel measurable)": [[3, "theorem-26"]], " (Every increasing function is Borel measurable)": [[3, "theorem-27"]], " (Example CFG for mathematical expressions)": [[29, "example-4"]], " (Example CFG)": [[29, "example-3"]], " (Examples of metric spaces)": [[31, "example-1"]], " (Examples of norms)": [[31, "topology:examples-of-norms"]], " (Expectation of continuous random variable)": [[17, "definition-5"]], " (Expectation of product of independent variables)": [[15, "theorem-4"]], " (Expectation)": [[14, "definition-3"]], " (FSA accepts a string / recognises a language)": [[28, "definition-2"]], " (Fenchel-Legendre transform)": [[20, "definition-8"]], " (Finite State Automaton; FSA)": [[28, "finite-state-automaton"]], " (Generalised nondeterministic finite automaton)": [[28, "definition-17"]], " (Generating function)": [[16, "definition-0"]], " (Heine-Borel)": [[3, "mira:thm:heine-borel"]], " (Hoeffding for RFF)": [[7, "lemma-2"]], " (Importance weighted MCMC algorithm)": [[4, "definition-1"]], " (Independence \\iff expectations of products of functions factorise)": [[18, "theorem-7"]], " (Independence \\iff expected product of functions factorises)": [[15, "theorem-5"]], " (Independence \\iff pdf factorises)": [[18, "theorem-2"]], " (Independence \\iff pmf factorises)": [[15, "theorem-3"]], " (Independence \\implies G factorises)": [[16, "theorem-5"]], " (Independence \\implies mgf of sum factorises)": [[19, "theorem-7"]], " (Independence of variables)": [[18, "definition-1"]], " (Independence)": [[13, "definition-4"], [15, "definition-2"]], " (Indicator functions)": [[15, "definition-7"]], " (Inequalities with Riemann sums)": [[2, "mira-thm-refining-partitions"]], " (Infimum and supremum of a sequence of measurable functions is measurable)": [[3, "theorem-34"]], " (Infimum and supremum)": [[2, "definition-1"]], " (Inner product)": [[31, "topology:inner-product-space"]], " (Inner products induce norms)": [[31, "topology:lemma-inner-products-induce-norms"]], " (Interchanging Riemann integral and limit)": [[2, "theorem-14"]], " (Inverse image of a composition)": [[3, "theorem-22"]], " (Inverse image)": [[3, "definition-20"]], " (Inversion theorem)": [[19, "theorem-16"]], " (Ito\u2019s result for one dimension)": [[6, "theorem-5"]], " (Jacobian formula)": [[18, "theorem-4"]], " (Jensen\u2019s inequality)": [[19, "theorem-11"]], " (Joint distribution function)": [[18, "definition-0"]], " (Joint probability 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" (Open neighbourhood)": [[31, "topology:def-open-neighbourhood"]], " (Other properties of \\sigma-algebras)": [[3, "theorem-16"]], " (Outer measure is additive if sets are contained by disjoint open intervals)": [[3, "mira:thm:outer-measure-is-additive-if-sets-are-separable"]], " (Outer measure is countably subadditive)": [[3, "mira:thm:countable-subadditivity-of-outer-measure"]], " (Outer measure is translation invariant)": [[3, "theorem-5"]], " (Outer measure of a closed interval)": [[3, "mira:thm:outer-measure-of-a-closed-interval"]], " (Outer measure preserves order)": [[3, "mira:thm:outer-measure-preserves-order"]], " (Outer measure)": [[3, "definition-1"]], " (PDA accepts)": [[29, "definition-10"]], " (Partition theorem for conditional expectations)": [[14, "theorem-8"]], " (Partition theorem)": [[13, "theorem-6"]], " (Partition)": [[2, "definition-0"]], " (Pdf of a function of a random variable)": [[17, "theorem-4"]], " (Pointwise limit of S-measurable functions is 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[[28, "lemma-16"]], " (Regular expressions)": [[28, "toc-def-regex"]], " (Regular laguages \\equiv regular expressions)": [[28, "corollary-20"]], " (Regular language)": [[28, "definition-4"]], " (Regular languages can be written as regular expressions)": [[28, "lemma-18"]], " (Regular operations)": [[28, "definition-7"]], " (Riemann integral)": [[2, "definition-5"], [2, "definition-7"]], " (Riemann integration does not work with limits)": [[2, "example-12"]], " (Riemann integration does not work with pointwise limits)": [[2, "example-13"]], " (Riemann integration does not work with unbounded functions)": [[2, "example-11"]], " (Score matching objective)": [[8, "definition-0"]], " (Smallest \\sigma-algebra containing a collection of subsets)": [[3, "theorem-18"]], " (Some regular expressions)": [[28, "example-9"]], " (Stein variational gradient descent)": [[9, "definition-2"]], " (Strings and Languages)": [[28, "definition-1"]], " (Strong convergence)": [[6, "definition-2"]], " (Sufficient condition for measurable function)": [[3, "mira-thm-sufficient-condition-measurable-with-infinity"]], " (Supporting tangent theorem)": [[19, "theorem-12"]], " (Translation invariance)": [[3, "mira:thm:outer-measure-is-translation-invariant"]], " (Two properties of characteristic functions)": [[19, "theorem-14"]], " (Two results for discrete random variables)": [[14, "theorem-5"]], " (Uniform convergence of RFF)": [[7, "lemma-3"]], " (Uniqueness of characteristic functions)": [[19, "theorem-15"]], " (Uniqueness of mgfs)": [[19, "theorem-8"]], " (Uniqueness of pgfs)": [[16, "theorem-2"]], " (Uniqueness theorem for moments)": [[19, "theorem-0"]], " (Upper and lower Riemann sums)": [[2, "definition-2"]], " (Variance)": [[14, "definition-6"]], " (Weak convergence)": [[6, "definition-3"]], " (Weak law of large numbers)": [[20, "theorem-5"]], " (Wiener process)": [[6, "definition-0"]], " (Yields, derives)": [[29, "definition-1"]], " (\\sigma-algebra)": [[3, 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"implementation"]], "Importance sampling": [[4, "importance-sampling"]], "Importance-weighted MCMC": [[4, "importance-weighted-mcmc"]], "Independence and sums": [[18, "independence-and-sums"]], "Indicator functions": [[15, "indicator-functions"]], "Inner products": [[31, "inner-products"]], "Introduction to transformers": [[11, "introduction-to-transformers"]], "Invariant distributions": [[24, "invariant-distributions"]], "Invertible transformations": [[9, "invertible-transformations"]], "Joint distributions": [[18, "joint-distributions"]], "Lack of memory property": [[23, "lack-of-memory-property"]], "Large deviations": [[20, "large-deviations"]], "Lebesgue measure": [[3, "lebesgue-measure"]], "Limits of characteristic functions": [[20, "limits-of-characteristic-functions"]], "Main limit theorems": [[20, "main-limit-theorems"]], "Markov and Jensen inequalities": [[19, "markov-and-jensen-inequalities"]], "Markov chain and property": [[24, "markov-chain-and-property"]], "Markov chains": 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