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		<id>https://en.formulasearchengine.com/index.php?title=Equilibrium_unfolding&amp;diff=14832</id>
		<title>Equilibrium unfolding</title>
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		<summary type="html">&lt;p&gt;128.61.115.251: /* Chemical denaturation */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;In [[mathematics]], a &#039;&#039;&#039;sample-continuous process&#039;&#039;&#039; is a [[stochastic process]] whose sample paths are [[almost surely]] [[continuous function]]s.&lt;br /&gt;
&lt;br /&gt;
==Definition==&lt;br /&gt;
&lt;br /&gt;
Let (&amp;amp;Omega;,&amp;amp;nbsp;&amp;amp;Sigma;,&amp;amp;nbsp;&#039;&#039;&#039;P&#039;&#039;&#039;) be a [[probability space]]. Let &#039;&#039;X&#039;&#039;&amp;amp;nbsp;:&amp;amp;nbsp;&#039;&#039;I&#039;&#039;&amp;amp;nbsp;&amp;amp;times;&amp;amp;nbsp;&amp;amp;Omega;&amp;amp;nbsp;&amp;amp;rarr;&amp;amp;nbsp;&#039;&#039;S&#039;&#039; be a stochastic process, where the [[index set]] &#039;&#039;I&#039;&#039; and state space &#039;&#039;S&#039;&#039; are both [[topological space]]s. Then the process &#039;&#039;X&#039;&#039; is called &#039;&#039;&#039;sample-continuous&#039;&#039;&#039; (or &#039;&#039;&#039;almost surely continuous&#039;&#039;&#039;, or simply &#039;&#039;&#039;continuous&#039;&#039;&#039;) if the map &#039;&#039;X&#039;&#039;(&#039;&#039;&amp;amp;omega;&#039;&#039;)&amp;amp;nbsp;:&amp;amp;nbsp;&#039;&#039;I&#039;&#039;&amp;amp;nbsp;&amp;amp;rarr;&amp;amp;nbsp;&#039;&#039;S&#039;&#039; is [[Continuous function (topology)|continuous as a function of topological spaces]] for &#039;&#039;&#039;P&#039;&#039;&#039;-[[almost all]] &#039;&#039;&amp;amp;omega;&#039;&#039; in &#039;&#039;&amp;amp;Omega;&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
In many examples, the index set &#039;&#039;I&#039;&#039; is an interval of time, [0,&amp;amp;nbsp;&#039;&#039;T&#039;&#039;] or [0,&amp;amp;nbsp;+&amp;amp;infin;), and the state space &#039;&#039;S&#039;&#039; is the [[real line]] or &#039;&#039;n&#039;&#039;-[[dimension]]al [[Euclidean space]] &#039;&#039;&#039;R&#039;&#039;&#039;&amp;lt;sup&amp;gt;&#039;&#039;n&#039;&#039;&amp;lt;/sup&amp;gt;.&lt;br /&gt;
&lt;br /&gt;
==Examples==&lt;br /&gt;
&lt;br /&gt;
* [[Brownian motion]] (the [[Wiener process]]) on Euclidean space is sample-continuous.&lt;br /&gt;
* For &amp;quot;nice&amp;quot; parameters of the equations, solutions to [[stochastic differential equation]]s are sample-continuous. See the existence and uniqueness theorem in the stochastic differential equations article for some sufficient conditions to ensure sample continuity.&lt;br /&gt;
* The process &#039;&#039;X&#039;&#039;&amp;amp;nbsp;:&amp;amp;nbsp;[0,&amp;amp;nbsp;+&amp;amp;infin;)&amp;amp;nbsp;&amp;amp;times;&amp;amp;nbsp;&amp;amp;Omega;&amp;amp;nbsp;&amp;amp;rarr;&amp;amp;nbsp;&#039;&#039;&#039;R&#039;&#039;&#039; that makes equiprobable jumps up or down every unit time according to&lt;br /&gt;
&lt;br /&gt;
::&amp;lt;math&amp;gt;\begin{cases} X_{t} \sim \mathrm{Unif} (\{X_{t-1} - 1, X_{t-1} + 1\}), &amp;amp; t \mbox{ an integer;} \\ X_{t} = X_{\lfloor t \rfloor}, &amp;amp; t \mbox{ not an integer;} \end{cases}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
: is &#039;&#039;not&#039;&#039; sample-continuous. In fact, it is surely discontinuous.&lt;br /&gt;
&lt;br /&gt;
==Properties==&lt;br /&gt;
&lt;br /&gt;
* For sample-continuous processes, the [[finite-dimensional distribution]]s determine the [[Law (stochastic processes)|law]], and vice versa.&lt;br /&gt;
&lt;br /&gt;
==See also==&lt;br /&gt;
&lt;br /&gt;
* [[Continuous stochastic process]]&lt;br /&gt;
&lt;br /&gt;
==References==&lt;br /&gt;
&lt;br /&gt;
* {{cite book&lt;br /&gt;
|   author = Kloeden, Peter E.&lt;br /&gt;
|coauthors = Platen, Eckhard&lt;br /&gt;
|    title = Numerical solution of stochastic differential equations&lt;br /&gt;
|   series = Applications of Mathematics (New York) 23&lt;br /&gt;
|publisher = Springer-Verlag&lt;br /&gt;
| location = Berlin&lt;br /&gt;
|     year = 1992&lt;br /&gt;
|    pages = 38&amp;amp;ndash;39;&lt;br /&gt;
|     isbn = 3-540-54062-8&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Stochastic processes]]&lt;/div&gt;</summary>
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