Example 1 (Mixture of K Poisson distributions). The sample spaces are X = Z+ := {0, 1, 2, . . . } = and Y = [K] := {1, 2, . . . ,K}. The parameter space is Θ = ∆K−1 × R++, where ∆K−1 := {π = (π1, π2, . . . , πK) ∈ R+ : ∑K j=1 πj = 1}, R+ := {t ∈ R : t ≥ 0}, and R++ := {t ∈ R : t > 0}. Each distribution Pθ in P = {Pθ : θ ∈ Θ} is as follows. If (X,Y ) ∼ Pθ for θ = (π, λ1, λ2, . . . , λK), then Y ...

We show how to associate an R-tree to the set of cut points of a continuum. If X is a continuum without cut points we show how to associate an R-tree to the set of cut pairs of X . AMS Classification 54F15,54F05,20F65,20E08

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...